NYTimes:Blogs:: Popular classification:: mRMRe
bdanalytics
Date: (Fri) Jun 05, 2015
Introduction:
Data: Source: Training: https://kaggle2.blob.core.windows.net/competitions-data/kaggle/4347/NYTimesBlogTrain.csv New: https://kaggle2.blob.core.windows.net/competitions-data/kaggle/4347/NYTimesBlogTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://kaggle2.blob.core.windows.net/competitions-data/kaggle/4347/NYTimesBlogTrain.csv"
glb_newdt_url <- "https://kaggle2.blob.core.windows.net/competitions-data/kaggle/4347/NYTimesBlogTest.csv"
glb_out_pfx <- "NYTBlogs_mRMRe_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newent_dataset <- TRUE # or TRUE
glb_split_entity_newent_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- "<col_name> <condition_operator> <value>" # or NULL
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_max_fitent_obs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- TRUE; glb_is_binomial <- TRUE
glb_rsp_var_raw <- "Popular"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Popular.fctr"
# if the response factor is based on numbers e.g (0/1 vs. "A"/"B"),
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
relevel(factor(ifelse(raw == 1, "Y", "N")), as.factor(c("Y", "N")), ref="N")
#as.factor(paste0("B", raw))
#as.factor(raw)
}
glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0))## [1] Y Y N N N
## Levels: N Yglb_map_rsp_var_to_raw <- function(var) {
as.numeric(var) - 1
#as.numeric(var)
#levels(var)[as.numeric(var)]
#c(" <=50K", " >50K")[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0)))## [1] 1 1 0 0 0if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# NewsDesk = the New York Times desk that produced the story
# SectionName = the section the article appeared in (Opinion, Arts, Technology, etc.)
# SubsectionName = the subsection the article appeared in (Education, Small Business, Room for Debate, etc.)
# Headline = the title of the article
# Snippet = a small portion of the article text
# Abstract = a summary of the blog article, written by the New York Times
# WordCount = the number of words in the article
# created WordCount.log
# UniqueID = a unique identifier for each article
glb_id_vars <- c("UniqueID") # or NULL
glb_category_vars <- c("myCategory") # or NULL
# PubDate = the publication date, in the format "Year-Month-Day Hour:Minute:Second"
glb_date_vars <- c("PubDate")
glb_txt_vars <- c("Headline", "Snippet", "Abstract")
#Sys.setlocale("LC_ALL", "C") # For english
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
#dsp_obs(Headline.contains="polit")
#subset(glb_allobs_df, H.T.compani > 0)[, c("UniqueID", "Headline", "H.T.compani")]
glb_append_stop_words[["Headline"]] <- c(NULL
# Highly correlated to other features
# , "billion", "clip", "politics", "read", "springsummer"
)
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_append_stop_words[["Snippet"]] <- c(NULL
)
glb_append_stop_words[["Abstract"]] <- c(NULL
)
glb_important_terms <- list()
# Remember to use stemmed terms
glb_sprs_thresholds <- c(0.988, 0.970, 0.970) # Generates 29, 22, 22 terms
#glb_sprs_thresholds <- c(0.990, 0.970, 0.970) # Generates 41, 22, 22 terms
#glb_sprs_thresholds <- c(0.985, 0.970, 0.970) # Generates 16, 22, 22 terms
#glb_sprs_thresholds <- c(0.975, 0.965, 0.965) # Generates 08, 14, 14 terms
#glb_sprs_thresholds <- c(0.982, 0.980, 0.980) # Generates 10, 61, 62 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
glb_log_vars <- c("WordCount") # or NULL
# List transformed vars
glb_exclude_vars_as_features <- c(NULL) # or c("<var_name>")
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
if (!is.null(glb_txt_vars))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
# Remove PubDate.year since all entity data is from 2014
# Remove PubDate.month.fctr since all newent data is from December
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c("PubDate.year", "PubDate.month.fctr"))
glb_interaction_only_features <- NULL # or ???
glb_force_0_to_NA_vars <- c("WordCount.log") # or NULL
glb_impute_na_data <- TRUE # or FALSE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- TRUE # or FALSE
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
#glb_models_method_vctr <- c("glm", "bayesglm", "rpart", "rf") else
glb_models_method_vctr <- c("glm", "bayesglm", "rpart") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL # or "<model_id_prefix>.<model_method>"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 8.938 NA NAStep 1.0: import data
chunk option: eval=
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/NYTimesBlogTrain.csv..."
## [1] "dimensions of data in ./data/NYTimesBlogTrain.csv: 6,532 rows x 10 cols"
## NewsDesk SectionName SubsectionName
## 1 Business Crosswords/Games
## 2 Culture Arts
## 3 Business Business Day Dealbook
## 4 Business Business Day Dealbook
## 5 Science Health
## 6 Science Health
## Headline
## 1 More School Daze
## 2 New 96-Page Murakami Work Coming in December
## 3 Public Pension Funds Stay Mum on Corporate Expats
## 4 Boot Camp for Bankers
## 5 Of Little Help to Older Knees
## 6 A Benefit of Legal Marijuana
## Snippet
## 1 A puzzle from Ethan Cooper that reminds me that a bill is due.
## 2 The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His Years of Pilgrimage.
## 3 Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little about the strategy, which could hurt the nations tax base.
## 4 As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service members ideal customers.
## 5 Middle-aged and older patients are unlikely to benefit in the long term from surgery to repair tears in the meniscus, pads of cartilage in the knee, a new review of studies has found.
## 6 A new study has found evidence that legal access to marijuana is associated with fewer opioid overdose deaths, but researchers said their findings should not be used as the basis for the wide adoption of legalized cannabis.
## Abstract
## 1 A puzzle from Ethan Cooper that reminds me that a bill is due.
## 2 The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His Years of Pilgrimage.
## 3 Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little about the strategy, which could hurt the nations tax base.
## 4 As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service members ideal customers.
## 5 Middle-aged and older patients are unlikely to benefit in the long term from surgery to repair tears in the meniscus, pads of cartilage in the knee, a new review of studies has found.
## 6 A new study has found evidence that legal access to marijuana is associated with fewer opioid overdose deaths, but researchers said their findings should not be used as the basis for the wide adoption of legalized cannabis.
## WordCount PubDate Popular UniqueID
## 1 508 2014-09-01 22:00:09 1 1
## 2 285 2014-09-01 21:14:07 0 2
## 3 1211 2014-09-01 21:05:36 0 3
## 4 1405 2014-09-01 20:43:34 1 4
## 5 181 2014-09-01 18:58:51 1 5
## 6 245 2014-09-01 18:52:22 1 6
## NewsDesk SectionName SubsectionName
## 226 Styles
## 995
## 3327
## 4753 Multimedia
## 4802 Business Crosswords/Games
## 6463 TStyle
## Headline
## 226 For Tavi Gevinson, Fashion Takes a Back Seat, for Now
## 995 Reconsidering What to Call an Extremist Group
## 3327 Clinton's Diagnosis of What's Wrong With Politics
## 4753 'Off Color' and on Target About Race in America
## 4802 Daniel Finkel's Circle-Toss Game
## 6463 Entering the Void
## Snippet
## 226 Tavi Gevinson, the teenage fashion star turned Broadway actress, wont be much of a player at New York Fashion Week this season.
## 995 Editors have decided to adjust how The Times refer to an Islamic extremist group that controls territory in Syria and Iraq.
## 3327 Hillary Rodham Clinton continued to laugh off questions about her presidential aspirations on Tuesday, but she did shed some light on what she thinks is wrong in Washington.
## 4753 Off Color, a New York Times video series, looks at how artists of color are making sharp social commentary about race in America through comedy and performance.
## 4802 By math educator Daniel Finkel, a puzzle thats childs play. Can you figure it out?
## 6463 The Spanish artist Miquel Barcel closely examines the basic materials of life in response to Edward Hirsch questioning his own belief in a higher power.
## Abstract
## 226 Tavi Gevinson, the teenage fashion star turned Broadway actress, wont be much of a player at New York Fashion Week this season.
## 995 Editors have decided to adjust how The Times refer to an Islamic extremist group that controls territory in Syria and Iraq.
## 3327 Hillary Rodham Clinton continued to laugh off questions about her presidential aspirations on Tuesday, but she did shed some light on what she thinks is wrong in Washington.
## 4753 Off Color, a New York Times video series, looks at how artists of color are making sharp social commentary about race in America through comedy and performance.
## 4802 By math educator Daniel Finkel, a puzzle thats childs play. Can you figure it out?
## 6463 The Spanish artist Miquel Barcel closely examines the basic materials of life in response to Edward Hirsch questioning his own belief in a higher power.
## WordCount PubDate Popular UniqueID
## 226 459 2014-09-04 16:55:57 0 226
## 995 301 2014-09-15 16:05:13 0 995
## 3327 236 2014-10-14 14:45:51 0 3327
## 4753 393 2014-11-02 05:00:13 0 4753
## 4802 1628 2014-11-03 12:00:04 1 4802
## 6463 264 2014-11-27 12:00:09 0 6463
## NewsDesk SectionName SubsectionName
## 6527 Foreign
## 6528 Opinion Room For Debate
## 6529 Foreign
## 6530 TStyle
## 6531 Multimedia
## 6532 Business
## Headline
## 6527 1914: Russians Dominate in East Poland
## 6528 Finding a Secretary of Defense
## 6529 1889: Metropolitan Opera House Reopens in New York
## 6530 The Daily Gift: Picasso Plates for Creative Dining
## 6531 Racing From New York to Barcelona
## 6532 Math Anxiety: Why Hollywood Makes Robots of Alan Turing and Other Geniuses
## Snippet
## 6527 From the International Herald Tribune archives: Russians dominate in East Poland in 1914.
## 6528 If Chuck Hagel isn't the right Pentagon chief to respond to an onslaught of global crises, who is?
## 6529 From the International Herald Tribune archives: The Metropolitan Opera House reopens in New York in 1889.
## 6530 Each day until Christmas, the editors of T share a new holiday gift idea.
## 6531 A sailboat race from New York to Barcelona was the setting for a thrilling and sometimes terrifying video about this challenging sport.
## 6532 The visionary who stares at formulas written on walls or mirrors or better yet, thin air has become a Hollywood trope. So has the depiction of the genius who cant connect with real people.
## Abstract
## 6527 From the International Herald Tribune archives: Russians dominate in East Poland in 1914.
## 6528 If Chuck Hagel isn't the right Pentagon chief to respond to an onslaught of global crises, who is?
## 6529 From the International Herald Tribune archives: The Metropolitan Opera House reopens in New York in 1889.
## 6530 Each day until Christmas, the editors of T share a new holiday gift idea.
## 6531 A sailboat race from New York to Barcelona was the setting for a thrilling and sometimes terrifying video about this challenging sport.
## 6532 The visionary who stares at formulas written on walls or mirrors or better yet, thin air has become a Hollywood trope. So has the depiction of the genius who cant connect with real people.
## WordCount PubDate Popular UniqueID
## 6527 176 2014-11-30 13:48:40 0 6527
## 6528 1597 2014-11-30 13:27:23 0 6528
## 6529 214 2014-11-30 09:44:57 0 6529
## 6530 61 2014-11-30 09:00:43 0 6530
## 6531 441 2014-11-30 09:00:22 0 6531
## 6532 921 2014-11-30 07:00:40 0 6532
## 'data.frame': 6532 obs. of 10 variables:
## $ NewsDesk : chr "Business" "Culture" "Business" "Business" ...
## $ SectionName : chr "Crosswords/Games" "Arts" "Business Day" "Business Day" ...
## $ SubsectionName: chr "" "" "Dealbook" "Dealbook" ...
## $ Headline : chr "More School Daze" "New 96-Page Murakami Work Coming in December" "Public Pension Funds Stay Mum on Corporate Expats" "Boot Camp for Bankers" ...
## $ Snippet : chr "A puzzle from Ethan Cooper that reminds me that a bill is due." "The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His"| __truncated__ "Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little"| __truncated__ "As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service "| __truncated__ ...
## $ Abstract : chr "A puzzle from Ethan Cooper that reminds me that a bill is due." "The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His"| __truncated__ "Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little"| __truncated__ "As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service "| __truncated__ ...
## $ WordCount : int 508 285 1211 1405 181 245 258 893 1077 188 ...
## $ PubDate : chr "2014-09-01 22:00:09" "2014-09-01 21:14:07" "2014-09-01 21:05:36" "2014-09-01 20:43:34" ...
## $ Popular : int 1 0 0 1 1 1 0 1 1 0 ...
## $ UniqueID : int 1 2 3 4 5 6 7 8 9 10 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULLif (glb_is_separate_newent_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newent_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newent_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/NYTimesBlogTest.csv..."
## [1] "dimensions of data in ./data/NYTimesBlogTest.csv: 1,870 rows x 9 cols"
## NewsDesk SectionName SubsectionName
## 1 Culture
## 2 Culture Arts
## 3 Business Crosswords/Games
## 4 Business Business Day Dealbook
## 5 Science Health
## 6 Science Health
## Headline
## 1 'Birdman' Tops the Gothams
## 2 'Sleepy Hollow' Recap: A Not-So-Shocking Death
## 3 Drinking Buddy For Falstaff
## 4 Encouraging Public Service, Through Wall Street's 'Revolving Door'
## 5 Therapy Prevents Repeat Suicide Attempts
## 6 Hoping for a Good Death
## Snippet
## 1 The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner.
## 2 In the fall season finale, a question of where the series has many places to go.
## 3 In which Timothy Polin reveals his potty mouth.
## 4 The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than on good public policy.
## 5 Short-term psychotherapy may be an effective way to prevent repeated suicide attempts.
## 6 What I hadnt considered before my fathers heart attack was the precise meaning of not wanting to live hooked up to machines.
## Abstract
## 1 The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner.
## 2 In the fall season finale, a question of where the series has many places to go.
## 3 In which Timothy Polin reveals his potty mouth.
## 4 The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than on good public policy.
## 5 Short-term psychotherapy may be an effective way to prevent repeated suicide attempts.
## 6 What I hadnt considered before my fathers heart attack was the precise meaning of not wanting to live hooked up to machines.
## WordCount PubDate UniqueID
## 1 111 2014-12-01 22:45:24 6533
## 2 558 2014-12-01 22:01:34 6534
## 3 788 2014-12-01 22:00:26 6535
## 4 915 2014-12-01 21:04:13 6536
## 5 213 2014-12-01 19:13:20 6537
## 6 938 2014-12-01 19:05:12 6538
## NewsDesk SectionName SubsectionName
## 3 Business Crosswords/Games
## 334 OpEd Opinion
## 725 TStyle
## 732 Business Business Day Dealbook
## 752 Business Business Day Dealbook
## 864
## Headline
## 3 Drinking Buddy For Falstaff
## 334 Facts & Figures: America’s Unique Take on Maternity Leave
## 725 Ansel Elgort Buttons Up in Brioni
## 732 A Shake-Up as the Financial World Infiltrates Philanthropy
## 752 Coupang, a South Korean E-Commerce Site, Raises $300 Million
## 864 Today in Politics
## Snippet
## 3 In which Timothy Polin reveals his potty mouth.
## 334 In the U.S., paid parental leave is more of a perk than a guarantee.
## 725 The actor brought a tinge of youthfulness to the classic Italian houses retro-tailored look.
## 732 Donor-advised funds help investors get deductions for charitable donations in one year, but society doesnt get the benefit of the money right away.
## 752 The latest financing round underscores Coupangs maturity and its ambitions to one day be a publicly traded company.
## 864 The 113th Congress is concluding with partisan brinksmanship and one last mad scramble for votes to pass a $1.1 trillion spending package.
## Abstract
## 3 In which Timothy Polin reveals his potty mouth.
## 334 In the U.S., paid parental leave is more of a perk than a guarantee.
## 725 The actor brought a tinge of youthfulness to the classic Italian houses retro-tailored look.
## 732 Donor-advised funds help investors get deductions for charitable donations in one year, but society doesnt get the benefit of the money right away.
## 752 The latest financing round underscores Coupangs maturity and its ambitions to one day be a publicly traded company.
## 864 The 113th Congress is concluding with partisan brinksmanship and one last mad scramble for votes to pass a $1.1 trillion spending package.
## WordCount PubDate UniqueID
## 3 788 2014-12-01 22:00:26 6535
## 334 160 2014-12-04 11:45:20 6866
## 725 89 2014-12-10 12:30:47 7257
## 732 1172 2014-12-10 12:00:38 7264
## 752 353 2014-12-10 08:30:41 7284
## 864 1544 2014-12-11 07:09:25 7396
## NewsDesk SectionName SubsectionName
## 1865
## 1866 Business Technology
## 1867 Metro N.Y. / Region
## 1868 Multimedia
## 1869 Foreign World Asia Pacific
## 1870 Science Health
## Headline
## 1865 Today in Politics
## 1866 Uber Suspends Operations in Spain
## 1867 New York Today: The Year in News
## 1868 New Year, Old Memories, in Times Square
## 1869 Hong Kong Police Criticized After 14-Year-Old's Detention
## 1870 The Super-Short Workout and Other Fitness Trends
## Snippet
## 1865 House Republicans are ending the year on a defensive note over Representative Steve Scalises 2002 speech to a white supremacist group.
## 1866 In a first in the growing pushback against Ubers global expansion, a judges ruling barred telecommunications operators and banks from supporting the companys services.
## 1867 Wednesday: The most read stories of 2014, teeth-chattering cold, and its New Years Eve.
## 1868 What happens when you combine Burning Man, Independence Day fireworks, the last day of school and a full-contact Black Friday sale-a-bration? New Years Eve in Times Square.
## 1869 The authorities have been accused of trying to intimidate young pro-democracy protesters and their families after a 14-year-old girl was detained on suspicion of drawing flowers in chalk near government headquarters and sent to a juvenile home.
## 1870 The big story in exercise science this year was the super-short workout, although many other fitness-related themes emerged in 2014.
## Abstract
## 1865 House Republicans are ending the year on a defensive note over Representative Steve Scalises 2002 speech to a white supremacist group.
## 1866 In a first in the growing pushback against Ubers global expansion, a judges ruling barred telecommunications operators and banks from supporting the companys services.
## 1867 Wednesday: The most read stories of 2014, teeth-chattering cold, and its New Years Eve.
## 1868 What happens when you combine Burning Man, Independence Day fireworks, the last day of school and a full-contact Black Friday sale-a-bration? New Years Eve in Times Square.
## 1869 The authorities have been accused of trying to intimidate young pro-democracy protesters and their families after a 14-year-old girl was detained on suspicion of drawing flowers in chalk near government headquarters and sent to a juvenile home.
## 1870 The big story in exercise science this year was the super-short workout, although many other fitness-related themes emerged in 2014.
## WordCount PubDate UniqueID
## 1865 1616 2014-12-31 07:03:46 8397
## 1866 292 2014-12-31 06:09:32 8398
## 1867 1010 2014-12-31 06:06:58 8399
## 1868 387 2014-12-31 05:00:19 8400
## 1869 717 2014-12-31 04:16:29 8401
## 1870 818 2014-12-31 00:01:10 8402
## 'data.frame': 1870 obs. of 9 variables:
## $ NewsDesk : chr "Culture" "Culture" "Business" "Business" ...
## $ SectionName : chr "" "Arts" "Crosswords/Games" "Business Day" ...
## $ SubsectionName: chr "" "" "" "Dealbook" ...
## $ Headline : chr "'Birdman' Tops the Gothams" "'Sleepy Hollow' Recap: A Not-So-Shocking Death" "Drinking Buddy For Falstaff" "Encouraging Public Service, Through Wall Street's 'Revolving Door'" ...
## $ Snippet : chr "The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner." "In the fall season finale, a question of where the series has many places to go." "In which Timothy Polin reveals his potty mouth." "The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than "| __truncated__ ...
## $ Abstract : chr "The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner." "In the fall season finale, a question of where the series has many places to go." "In which Timothy Polin reveals his potty mouth." "The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than "| __truncated__ ...
## $ WordCount : int 111 558 788 915 213 938 1336 2644 752 99 ...
## $ PubDate : chr "2014-12-01 22:45:24" "2014-12-01 22:01:34" "2014-12-01 22:00:26" "2014-12-01 21:04:13" ...
## $ UniqueID : int 6533 6534 6535 6536 6537 6538 6539 6540 6541 6542 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
# Check for duplicates in glb_id_vars
if (length(glb_id_vars) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_id_vars <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_vars, FALSE])) > 0)
stop(glb_id_vars, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_vars)
# Combine trnent & newent into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
glb_trnobs_df <- glb_newobs_df <- NULL
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 8.938 9.955 1.017
## 2 inspect.data 2 0 9.956 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## Loading required package: reshape2
## Popular.0 Popular.1 Popular.NA
## Test NA NA 1870
## Train 5439 1093 NA
## Popular.0 Popular.1 Popular.NA
## Test NA NA 1
## Train 0.8326699 0.1673301 NA
## [1] "numeric data missing in glb_allobs_df: "
## Popular
## 1870
## [1] "numeric data w/ 0s in glb_allobs_df: "
## WordCount Popular
## 109 5439
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate
## 17 0# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## Popular Popular.fctr .n
## 1 0 N 5439
## 2 NA <NA> 1870
## 3 1 Y 1093## Warning in loop_apply(n, do.ply): Removed 1 rows containing missing values
## (position_stack).
## Popular.fctr.N Popular.fctr.Y Popular.fctr.NA
## Test NA NA 1870
## Train 5439 1093 NA
## Popular.fctr.N Popular.fctr.Y Popular.fctr.NA
## Test NA NA 1
## Train 0.8326699 0.1673301 NA# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
myextract_dates_df <- function(df, vars, rsp_var) {
keep_feats <- c(NULL)
for (var in vars) {
dates_df <- data.frame(.date=strptime(df[, var], "%Y-%m-%d %H:%M:%S"))
dates_df[, rsp_var] <- df[, rsp_var]
dates_df[, paste0(var, ".POSIX")] <- dates_df$.date
dates_df[, paste0(var, ".year")] <- as.numeric(format(dates_df$.date, "%Y"))
dates_df[, paste0(var, ".year.fctr")] <- as.factor(format(dates_df$.date, "%Y"))
dates_df[, paste0(var, ".month")] <- as.numeric(format(dates_df$.date, "%m"))
dates_df[, paste0(var, ".month.fctr")] <- as.factor(format(dates_df$.date, "%m"))
dates_df[, paste0(var, ".date")] <- as.numeric(format(dates_df$.date, "%d"))
dates_df[, paste0(var, ".date.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%d")), 5) # by month week
dates_df[, paste0(var, ".juliandate")] <- as.numeric(format(dates_df$.date, "%j"))
# wkday Sun=0; Mon=1; ...; Sat=6
dates_df[, paste0(var, ".wkday")] <- as.numeric(format(dates_df$.date, "%w"))
dates_df[, paste0(var, ".wkday.fctr")] <- as.factor(format(dates_df$.date, "%w"))
# Federal holidays 1.9., 13.10., 27.11., 25.12.
# NYState holidays 1.9., 13.10., 11.11., 27.11., 25.12.
months <- dates_df[, paste0(var, ".month")]
dates <- dates_df[, paste0(var, ".date")]
dates_df[, paste0(var, ".hlday")] <-
ifelse( ((months == 09) & (dates == 01)) |
((months == 10) & (dates == 13)) |
((months == 11) & (dates == 27)) |
((months == 12) & (dates == 25)) ,
1, 0)
dates_df[, paste0(var, ".wkend")] <- as.numeric(
(dates_df[, paste0(var, ".wkday")] %in% c(0, 6)) |
dates_df[, paste0(var, ".hlday")] )
dates_df[, paste0(var, ".hour")] <- as.numeric(format(dates_df$.date, "%H"))
dates_df[, paste0(var, ".hour.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%H")), 3) # by work-shift
dates_df[, paste0(var, ".minute")] <- as.numeric(format(dates_df$.date, "%M"))
dates_df[, paste0(var, ".minute.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%M")), 4) # by quarter-hours
dates_df[, paste0(var, ".second")] <- as.numeric(format(dates_df$.date, "%S"))
dates_df[, paste0(var, ".second.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%S")), 4) # by quarter-hours
dates_df[, paste0(var, ".day.minutes")] <- 60 * dates_df[, paste0(var, ".hour")] +
dates_df[, paste0(var, ".minute")]
dates_df[, paste0(var, ".day.minutes.poly.", 1:5)] <-
as.matrix(poly(dates_df[, paste0(var, ".day.minutes")], 5))
# print(gp <- myplot_box(df=dates_df, ycol_names="PubDate.day.minutes",
# xcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name=".rownames",
# ycol_name="PubDate.day.minutes", colorcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes.poly.1", colorcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.day.minutes",
# ycol_name="PubDate.day.minutes.poly.4", colorcol_name=rsp_var))
#
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes", colorcol_name=rsp_var, smooth=TRUE))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes.poly.4", colorcol_name=rsp_var, smooth=TRUE))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name=c("PubDate.day.minutes", "PubDate.day.minutes.poly.4"),
# colorcol_name=rsp_var))
print(gp <- myplot_scatter(df=subset(dates_df, Popular.fctr=="Y"),
xcol_name="PubDate.juliandate",
ycol_name="PubDate.day.minutes", colorcol_name=rsp_var))
print(gp <- myplot_box(df=dates_df, ycol_names="PubDate.second",
xcol_name=rsp_var))
print(gp <- myplot_bar(df=dates_df, ycol_names="PubDate.second.fctr",
xcol_name=rsp_var, colorcol_name="PubDate.second.fctr"))
keep_feats <- union(keep_feats, paste(var,
c(".POSIX", ".year.fctr", ".month.fctr", ".date.fctr", ".wkday.fctr",
".wkend", ".hour.fctr", ".minute.fctr", ".second.fctr",
paste0(".day.minutes.poly.", 1:5)), sep=""))
}
#myprint_df(dates_df)
return(dates_df[, keep_feats])
}
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars, rsp_var=glb_rsp_var))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
paste(glb_date_vars, c("", ".POSIX"), sep=""))
}
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
if (!is.null(glb_date_vars)) {
srt_allobs_df <- orderBy(~PubDate.POSIX, glb_allobs_df)
print(myplot_scatter(subset(srt_allobs_df,
PubDate.POSIX < strptime("2014-09-02", "%Y-%m-%d")),
xcol_name="PubDate.POSIX", ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var
))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
pd = as.POSIXlt(srt_allobs_df$PubDate)
z = zoo(as.numeric(pd))
srt_allobs_df[, "PubDate.zoo"] <- z
print(head(srt_allobs_df))
print(myplot_scatter(subset(srt_allobs_df,
PubDate.POSIX < strptime("2014-09-02", "%Y-%m-%d")),
xcol_name="PubDate.zoo", ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var
))
n = nrow(srt_allobs_df)
b = zoo(, seq(n))
last1 = as.numeric(merge(z-lag(z, -1), b, all = TRUE))
srt_allobs_df[, "PubDate.last1"] <- last1
srt_allobs_df[is.na(srt_allobs_df$PubDate.last1), "PubDate.last1"] <- 0
srt_allobs_df[, "PubDate.last1.log"] <- log(1 + srt_allobs_df[, "PubDate.last1"])
print(gp <- myplot_box(df=subset(srt_allobs_df, PubDate.last1.log > 0),
ycol_names="PubDate.last1.log",
xcol_name=glb_rsp_var))
last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
srt_allobs_df[, "PubDate.last10"] <- last10
srt_allobs_df[is.na(srt_allobs_df$PubDate.last10), "PubDate.last10"] <- 0
srt_allobs_df[, "PubDate.last10.log"] <- log(1 + srt_allobs_df[, "PubDate.last10"])
print(gp <- myplot_box(df=subset(srt_allobs_df, PubDate.last10.log > 0),
ycol_names="PubDate.last10.log",
xcol_name=glb_rsp_var))
last100 = as.numeric(merge(z-lag(z, -100), b, all = TRUE))
srt_allobs_df[, "PubDate.last100"] <- last100
srt_allobs_df[is.na(srt_allobs_df$PubDate.last100), "PubDate.last100"] <- 0
srt_allobs_df[, "PubDate.last100.log"] <- log(1 + srt_allobs_df[, "PubDate.last100"])
print(gp <- myplot_box(df=subset(srt_allobs_df, PubDate.last100.log > 0),
ycol_names="PubDate.last100.log",
xcol_name=glb_rsp_var))
glb_allobs_df <- srt_allobs_df
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c("PubDate.zoo", "PubDate.last1", "PubDate.last10", "PubDate.last100"))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## NewsDesk SectionName SubsectionName
## 33 Science Health
## 32 Foreign World Asia Pacific
## 31 Multimedia
## 30 Culture Arts
## 29 Business Business Day Dealbook
## 28 Magazine Magazine
## Headline
## 33 Don't Catch What Ails Your House
## 32 Ukraine Conflict Has Been a Lift for China, Scholars Say
## 31 Revisiting Life and Death in Africa
## 30 Fabio Luisi Has a New Gig
## 29 Heineken to Sell Mexican Packaging Unit to Crown Holdings
## 28 Behind the Cover Story: Emily Bazelon on Medical Abortion Through the Mail
## Snippet
## 33 It doesnt take a flood to encourage the growth of mold in a home. A moist environment will do. A runny nose, coughing and all the rest typically follow.
## 32 As the United States and the European Union have imposed sanctions on Russia over the unrest in eastern Ukraine, China has been able to stand apart and gain concrete advantages, experts on foreign policy say.
## 31 Yunghi Kim went to Somalia 20 years ago expecting to cover a famine. She found herself instead in a war zone.
## 30 The music director of the Zurich Opera and principal conductor of the Metropolitan Opera will be named principal conductor of the Danish National Symphony Orchestra.
## 29 The deal values the container unit Empaque at about $1.2 billion and would make Crown Holdings the second-largest beverage can producer in North America.
## 28 Emily Bazelon, a contributing writer for the magazine, wrote this weeks cover story about the online distribution of medical abortions. Here she discusses reporting on a group of activists working to provide medical abortions through the mail.
## Abstract
## 33 It doesnt take a flood to encourage the growth of mold in a home. A moist environment will do. A runny nose, coughing and all the rest typically follow.
## 32 As the United States and the European Union have imposed sanctions on Russia over the unrest in eastern Ukraine, China has been able to stand apart and gain concrete advantages, experts on foreign policy say.
## 31 Yunghi Kim went to Somalia 20 years ago expecting to cover a famine. She found herself instead in a war zone.
## 30 The music director of the Zurich Opera and principal conductor of the Metropolitan Opera will be named principal conductor of the Danish National Symphony Orchestra.
## 29 The deal values the container unit Empaque at about $1.2 billion and would make Crown Holdings the second-largest beverage can producer in North America.
## 28 Emily Bazelon, a contributing writer for the magazine, wrote this weeks cover story about the online distribution of medical abortions. Here she discusses reporting on a group of activists working to provide medical abortions through the mail.
## WordCount PubDate Popular UniqueID .src Popular.fctr
## 33 962 2014-09-01 00:01:32 1 33 Train Y
## 32 529 2014-09-01 02:48:41 0 32 Train N
## 31 832 2014-09-01 03:00:15 0 31 Train N
## 30 166 2014-09-01 04:00:06 0 30 Train N
## 29 442 2014-09-01 04:11:20 0 29 Train N
## 28 1190 2014-09-01 05:00:26 0 28 Train N
## PubDate.POSIX PubDate.year.fctr PubDate.month.fctr
## 33 2014-09-01 00:01:32 2014 09
## 32 2014-09-01 02:48:41 2014 09
## 31 2014-09-01 03:00:15 2014 09
## 30 2014-09-01 04:00:06 2014 09
## 29 2014-09-01 04:11:20 2014 09
## 28 2014-09-01 05:00:26 2014 09
## PubDate.date.fctr PubDate.wkday.fctr PubDate.wkend PubDate.hour.fctr
## 33 (0.97,7] 1 1 (-0.023,7.67]
## 32 (0.97,7] 1 1 (-0.023,7.67]
## 31 (0.97,7] 1 1 (-0.023,7.67]
## 30 (0.97,7] 1 1 (-0.023,7.67]
## 29 (0.97,7] 1 1 (-0.023,7.67]
## 28 (0.97,7] 1 1 (-0.023,7.67]
## PubDate.minute.fctr PubDate.second.fctr PubDate.day.minutes.poly.1
## 33 (-0.059,14.8] (29.5,44.2] -0.02745833
## 32 (44.2,59.1] (29.5,44.2] -0.02139413
## 31 (-0.059,14.8] (14.8,29.5] -0.02095838
## 30 (-0.059,14.8] (-0.059,14.8] -0.01877962
## 29 (-0.059,14.8] (14.8,29.5] -0.01838018
## 28 (-0.059,14.8] (14.8,29.5] -0.01660087
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 33 0.04254377 -0.044820236
## 32 0.02175312 -0.007057785
## 31 0.02046604 -0.005257251
## 30 0.01444664 0.002155510
## 29 0.01341829 0.003242535
## 28 0.00912060 0.007145007
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5 PubDate.zoo
## 33 0.03365827 -0.02362780 1409544092
## 32 -0.01440172 0.03180561 1409554121
## 31 -0.01557161 0.03121768 1409554815
## 30 -0.01830080 0.02423360 1409558406
## 29 -0.01831901 0.02246133 1409559080
## 28 -0.01700098 0.01380338 1409562026## Don't know how to automatically pick scale for object of type zoo. Defaulting to continuous
# check distribution of all numeric data
dsp_numeric_vars_dstrb <- function(vars_lst) {
for (var in vars_lst) {
print(sprintf("var: %s", var))
gp <- myplot_box(df=glb_allobs_df, ycol_names=var, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, var], "factor"))
gp <- gp + facet_wrap(reformulate(var))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
.rnorm=rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyr# Add logs of numerics that are not distributed normally -> do automatically ???
if (!is.null(glb_log_vars)) {
# Cycle thru glb_log_vars & create logs
# <col_name>.log=log(1 + <col.name>),
for (col in glb_log_vars)
glb_allobs_df[, paste0(col, ".log")] <- log(1 + glb_allobs_df[, col])
# Add raw_vars to glb_exclude_vars_as_features
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_log_vars)
}
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
union(myfind_chr_cols_df(glb_allobs_df),
union(glb_rsp_var_raw,
union(glb_rsp_var, glb_exclude_vars_as_features)))))## [1] "var: PubDate.year.fctr"
## [1] "var: PubDate.date.fctr"
## [1] "var: PubDate.wkday.fctr"
## [1] "var: PubDate.wkend"
## [1] "var: PubDate.hour.fctr"
## [1] "var: PubDate.minute.fctr"
## [1] "var: PubDate.second.fctr"
## [1] "var: PubDate.day.minutes.poly.1"
## [1] "var: PubDate.day.minutes.poly.2"
## [1] "var: PubDate.day.minutes.poly.3"
## [1] "var: PubDate.day.minutes.poly.4"
## [1] "var: PubDate.day.minutes.poly.5"
## [1] "var: PubDate.last1.log"
## [1] "var: PubDate.last10.log"
## [1] "var: PubDate.last100.log"
## [1] "var: .rnorm"
## [1] "var: WordCount.log"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("KO", "PG")),
# "Date.my", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.Date("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.Date("1983-01-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5))
rm(srt_allobs_df, last1, last10, last100, pd)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cleanse.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 9.956 31.672 21.716
## 3 cleanse.data 2 1 31.672 NA NAStep 2.1: cleanse data
# Options:
# 1. Not fill missing vars
# 2. Fill missing numerics with a different algorithm
# 3. Fill missing chars with data based on clusters
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## Popular Popular.fctr
## 1870 1870
## [1] "numeric data w/ 0s in : "
## WordCount Popular PubDate.wkday.fctr
## 109 5439 378
## PubDate.wkend PubDate.last1 PubDate.last1.log
## 7624 11 11
## PubDate.last10 PubDate.last10.log PubDate.last100
## 10 10 100
## PubDate.last100.log WordCount.log
## 100 109
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate
## 17 0if (!is.null(glb_force_0_to_NA_vars)) {
for (feat in glb_force_0_to_NA_vars) {
warning("Forcing ", sum(glb_allobs_df[, feat] == 0),
" obs with ", feat, " 0s to NAs")
glb_allobs_df[glb_allobs_df[, feat] == 0, feat] <- NA
}
}## Warning: Forcing 109 obs with WordCount.log 0s to NAsmycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## Popular Popular.fctr WordCount.log
## 1870 1870 109
## [1] "numeric data w/ 0s in : "
## WordCount Popular PubDate.wkday.fctr
## 109 5439 378
## PubDate.wkend PubDate.last1 PubDate.last1.log
## 7624 11 11
## PubDate.last10 PubDate.last10.log PubDate.last100
## 10 10 100
## PubDate.last100.log
## 100
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate
## 17 0dsp_catgs <- function() {
print("NewsDesk:")
print(table(glb_allobs_df$NewsDesk))
print("SectionName:")
print(table(glb_allobs_df$SectionName))
print("SubsectionName:")
print(table(glb_allobs_df$SubsectionName))
}
sel_obs <- function(Popular=NULL,
NewsDesk=NULL, SectionName=NULL, SubsectionName=NULL,
Headline.contains=NULL, Snippet.contains=NULL, Abstract.contains=NULL,
Headline.pfx=NULL, NewsDesk.nb=NULL, .clusterid=NULL, myCategory=NULL,
perl=FALSE) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
if (!is.null(Popular)) {
if (is.na(Popular))
tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
}
if (!is.null(NewsDesk))
tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
if (!is.null(SectionName))
tmp_df <- tmp_df[tmp_df$SectionName == SectionName, ]
if (!is.null(SubsectionName))
tmp_df <- tmp_df[tmp_df$SubsectionName == SubsectionName, ]
if (!is.null(Headline.contains))
tmp_df <-
tmp_df[grep(Headline.contains, tmp_df$Headline, perl=perl), ]
if (!is.null(Snippet.contains))
tmp_df <-
tmp_df[grep(Snippet.contains, tmp_df$Snippet, perl=perl), ]
if (!is.null(Abstract.contains))
tmp_df <-
tmp_df[grep(Abstract.contains, tmp_df$Abstract, perl=perl), ]
if (!is.null(Headline.pfx)) {
if (length(grep("Headline.pfx", names(tmp_df), fixed=TRUE, value=TRUE))
> 0) tmp_df <-
tmp_df[tmp_df$Headline.pfx == Headline.pfx, ] else
warning("glb_allobs_df does not contain Headline.pfx; ignoring that filter")
}
if (!is.null(NewsDesk.nb)) {
if (any(grepl("NewsDesk.nb", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$NewsDesk.nb == NewsDesk.nb, ] else
warning("glb_allobs_df does not contain NewsDesk.nb; ignoring that filter")
}
if (!is.null(.clusterid)) {
if (any(grepl(".clusterid", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$clusterid == clusterid, ] else
warning("glb_allobs_df does not contain clusterid; ignoring that filter") }
if (!is.null(myCategory)) {
if (!(myCategory %in% names(glb_allobs_df)))
tmp_df <-
tmp_df[tmp_df$myCategory == myCategory, ] else
warning("glb_allobs_df does not contain myCategory; ignoring that filter")
}
return(glb_allobs_df$UniqueID %in% tmp_df$UniqueID)
}
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
union(c("UniqueID", "Popular", "myCategory", "Headline"), cols), FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_tbl <- function(...) {
tmp_entity_df <- glb_allobs_df[sel_obs(...), ]
tmp_tbl <- table(tmp_entity_df$NewsDesk,
tmp_entity_df$SectionName,
tmp_entity_df$SubsectionName,
tmp_entity_df$Popular, useNA="ifany")
#print(names(tmp_tbl))
#print(dimnames(tmp_tbl))
print(tmp_tbl)
}
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Create myCategory <- NewsDesk#SectionName#SubsectionName
# Fix some data before merging categories
# glb_allobs_df[sel_obs(Headline.contains="Your Turn:", NewsDesk=""),
# "NewsDesk"] <- "Styles"
# glb_allobs_df[sel_obs(Headline.contains="School", NewsDesk="", SectionName="U.S.",
# SubsectionName=""),
# "SubsectionName"] <- "Education"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SectionName"] <- "Business Day"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SubsectionName"] <- "Small Business"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SectionName"] <- "Opinion"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SubsectionName"] <- "Room For Debate"
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName="", Popular=NA),
# "SubsectionName"] <- "Small Business"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(7973),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName=""),
# "SectionName"] <- "Technology"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5076, 5736, 5924, 5911, 6532),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(SectionName="Health"),
# "NewsDesk"] <- "Science"
# glb_allobs_df[sel_obs(SectionName="Travel"),
# "NewsDesk"] <- "Travel"
#
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SectionName"] <- ""
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SubsectionName"] <- ""
# glb_allobs_df[sel_obs(NewsDesk="Styles", SectionName="", SubsectionName="", Popular=1),
# "SectionName"] <- "U.S."
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5486),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
glb_allobs_df$myCategory <- paste(glb_allobs_df$NewsDesk,
glb_allobs_df$SectionName,
glb_allobs_df$SubsectionName,
sep="#")
dsp_obs( Headline.contains="Music:"
#,NewsDesk=""
#,SectionName=""
#,SubsectionName="Fashion & Style"
#,Popular=1 #NA
,cols= c("UniqueID", "Headline", "Popular", "myCategory",
"NewsDesk", "SectionName", "SubsectionName"),
all=TRUE)## UniqueID Popular myCategory
## 305 305 0 OpEd#Opinion#
## 844 844 1 OpEd#Opinion#
## 1331 1331 0 OpEd#Opinion#
## 1974 1974 0 OpEd#Opinion#
## 2563 2563 0 OpEd#Opinion#
## 3091 3091 0 OpEd#Opinion#
## 3589 3589 0 OpEd#Opinion#
## 4631 4631 0 OpEd#Opinion#
## 5125 5125 0 OpEd#Opinion#
## 5630 5630 0 OpEd#Opinion#
## 6095 6095 0 OpEd#Opinion#
## 6513 6513 1 OpEd#Opinion#
## 6927 6927 NA OpEd#Opinion#
## 7473 7473 NA #Opinion#
## 7931 7931 NA OpEd#Opinion#
## 8217 8217 NA OpEd#Opinion#
## Headline NewsDesk
## 305 Friday Night Music: Lucius Covers John Lennon OpEd
## 844 Friday Night Music: Cheryl Wheeler OpEd
## 1331 Friday Night Music: Cheryl Wheeler, Summer Fly OpEd
## 1974 Friday Night Music: Quilt OpEd
## 2563 Friday Night Music: Lucius in Asheville OpEd
## 3091 Friday Night Music: Sarah Jarosz and the Milk Carton Kids OpEd
## 3589 Friday Night Music: Lucius Covers the Kinks OpEd
## 4631 Friday Night Music: Amason OpEd
## 5125 Friday Night Music: Suzanne Vega, Jacob and the Angel OpEd
## 5630 Friday Night Music: Suzanne Vega, I Never Wear White OpEd
## 6095 Friday Night Music: Jessica Hernandez and the Deltas OpEd
## 6513 Saturday Morning Music: Stay Gold OpEd
## 6927 Friday Night Music: Lucius, Monsters OpEd
## 7473 Friday Night Music: Peter Gabriel, 1993
## 7931 Friday Night Music: The Roches, Winter Wonderland OpEd
## 8217 Friday Night Music: Sarah Jarosz and Aoife O'Donovan OpEd
## SectionName SubsectionName
## 305 Opinion
## 844 Opinion
## 1331 Opinion
## 1974 Opinion
## 2563 Opinion
## 3091 Opinion
## 3589 Opinion
## 4631 Opinion
## 5125 Opinion
## 5630 Opinion
## 6095 Opinion
## 6513 Opinion
## 6927 Opinion
## 7473 Opinion
## 7931 Opinion
## 8217 Opiniondsp_obs( Headline.contains="."
,NewsDesk=""
,SectionName="Opinion"
,SubsectionName=""
#,Popular=1 #NA
,cols= c("UniqueID", "Headline", "Popular", "myCategory",
"NewsDesk", "SectionName", "SubsectionName"),
all=TRUE)## UniqueID Popular myCategory
## 516 516 0 #Opinion#
## 918 918 0 #Opinion#
## 863 863 1 #Opinion#
## 855 855 0 #Opinion#
## 1334 1334 0 #Opinion#
## 7473 7473 NA #Opinion#
## 7445 7445 NA #Opinion#
## 7419 7419 NA #Opinion#
## 7505 7505 NA #Opinion#
## 7509 7509 NA #Opinion#
## Headline
## 516 This Is Life Among the Roma, Europes Forgotten People
## 918 What Might Happen If Iran Becomes America's Covert Ally?
## 863 Readers Respond: Wary Support for Obama's ISIS Strategy
## 855 Readers Respond: Becoming a Real Person
## 1334 Readers Respond: On Spanking and Switching
## 7473 Friday Night Music: Peter Gabriel, 1993
## 7445 Senate Committee Bothered to Authorize War Against Islamic State
## 7419 Joe on WNYCs Money Talking
## 7505 Rev. Dr. William Barber II on Todays Protest Movements
## 7509 Did Salaita Cross the Line of Civility?
## NewsDesk SectionName SubsectionName
## 516 Opinion
## 918 Opinion
## 863 Opinion
## 855 Opinion
## 1334 Opinion
## 7473 Opinion
## 7445 Opinion
## 7419 Opinion
## 7505 Opinion
## 7509 Opinion# Merge some categories
glb_allobs_df$myCategory <-
plyr::revalue(glb_allobs_df$myCategory, c(
"#Business Day#Dealbook" = "Business#Business Day#Dealbook",
"#Business Day#Small Business" = "Business#Business Day#Small Business",
"#Crosswords/Games#" = "Business#Crosswords/Games#",
"Business##" = "Business#Technology#",
"#Open#" = "Business#Technology#",
"#Technology#" = "Business#Technology#",
"#Arts#" = "Culture#Arts#",
"Culture##" = "Culture#Arts#",
"#World#Asia Pacific" = "Foreign#World#Asia Pacific",
"Foreign##" = "Foreign#World#",
"#N.Y. / Region#" = "Metro#N.Y. / Region#",
"#Opinion#" = "OpEd#Opinion#",
"OpEd##" = "OpEd#Opinion#",
"#Health#" = "Science#Health#",
"Science##" = "Science#Health#",
"Styles##" = "Styles##Fashion",
"Styles#Health#" = "Science#Health#",
"Styles#Style#Fashion & Style" = "Styles##Fashion",
"#Travel#" = "Travel#Travel#",
"Magazine#Magazine#" = "myOther",
"National##" = "myOther",
"National#U.S.#Politics" = "myOther",
"Sports##" = "myOther",
"Sports#Sports#" = "myOther",
"#U.S.#" = "myOther",
# "Business##Small Business" = "Business#Business Day#Small Business",
#
# "#Opinion#" = "#Opinion#Room For Debate",
"##" = "##"
# "Business##" = "Business#Business Day#Dealbook",
# "Foreign#World#" = "Foreign##",
# "#Open#" = "Other",
# "#Opinion#The Public Editor" = "OpEd#Opinion#",
# "Styles#Health#" = "Styles##",
# "Styles#Style#Fashion & Style" = "Styles##",
# "#U.S.#" = "#U.S.#Education",
))
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_allobs_df,
c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + NewsDesk + SectionName + SubsectionName ~
Popular.fctr, sum, value.var=".n"))
myprint_df(ctgry_cast_df)## myCategory NewsDesk SectionName SubsectionName
## 33 OpEd#Opinion# OpEd Opinion
## 36 Science#Health# Science Health
## 1 ##
## 11 Business#Crosswords/Games# Business Crosswords/Games
## 40 Styles#U.S.# Styles U.S.
## 7 Business#Business Day#Dealbook Business Business Day Dealbook
## N Y NA
## 33 113 407 141
## 36 73 119 55
## 1 1169 115 342
## 11 19 103 38
## 40 77 100 61
## 7 864 88 291
## myCategory NewsDesk SectionName
## 42 Travel#Travel# Travel Travel
## 5 #U.S.#Education U.S.
## 6 Business#Business Day#Dealbook Business Day
## 8 Business#Business Day#Small Business Business Day
## 12 Business#Technology# Open
## 28 myOther National U.S.
## SubsectionName N Y NA
## 42 115 1 31
## 5 Education 325 0 89
## 6 Dealbook 0 0 13
## 8 Small Business 1 0 4
## 12 4 0 1
## 28 Politics 2 0 0
## myCategory NewsDesk SectionName SubsectionName N Y NA
## 27 myOther National 2 0 0
## 28 myOther National U.S. Politics 2 0 0
## 29 myOther Sports 1 0 0
## 30 myOther Sports Sports 1 0 0
## 37 Science#Health# Styles Health 1 0 0
## 39 Styles##Fashion Styles Style Fashion & Style 2 0 0write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
useNA="ifany"))##
## N Y <NA>
## ## 1169 115 342
## #Multimedia# 139 2 52
## #Opinion#Room For Debate 61 1 20
## #Opinion#The Public Editor 4 16 10
## #U.S.#Education 325 0 89
## Business#Business Day#Dealbook 864 88 304
## Business#Business Day#Small Business 135 5 41
## Business#Crosswords/Games# 20 103 42
## Business#Technology# 288 51 114
## Culture#Arts# 626 50 244
## Foreign#World# 172 0 47
## Foreign#World#Asia Pacific 200 3 56
## Metro#N.Y. / Region# 181 17 67
## myOther 38 0 5
## OpEd#Opinion# 117 409 164
## Science#Health# 74 122 57
## Styles##Fashion 118 1 15
## Styles#U.S.# 77 100 61
## Travel#Travel# 116 1 35
## TStyle## 715 9 105dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_vars, "Popular")],
sel_df[, c(glb_id_vars, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
# Copy Headline into Snipper & Abstract if they are empty
# print(glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, c(glb_rsp_var, "Headline", "Snippet", "Abstract")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Snippet,
# c("UniqueID", "Headline", "Snippet")])
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Snippet"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Headline"]
#
# print(glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, c(glb_rsp_var, "Headline", "Snippet", "Abstract")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Abstract,
# c("UniqueID", "Headline", "Abstract")])
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Abstract"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Headline"]
# WordCount_0_df <- subset(glb_allobs_df, WordCount == 0)
# table(WordCount_0_df$Popular, WordCount_0_df$WordCount, useNA="ifany")
# myprint_df(WordCount_0_df[,
# c("UniqueID", "Popular", "WordCount", "Headline")])
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 cleanse.data 2 1 31.672 37.919 6.248
## 4 manage.missing.data 2 2 37.920 NA NAStep 2.2: manage missing data
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## Popular Popular.fctr WordCount.log
## 1870 1870 109
## [1] "numeric data w/ 0s in : "
## WordCount Popular PubDate.wkday.fctr
## 109 5439 378
## PubDate.wkend PubDate.last1 PubDate.last1.log
## 7624 11 11
## PubDate.last10 PubDate.last10.log PubDate.last100
## 10 10 100
## PubDate.last100.log
## 100
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate myCategory
## 17 0 0# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
# complete(mice()) changes attributes of factors even though values don't change
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col], inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
} ## Loading required package: mice
## Loading required package: Rcpp
## mice 2.22 2014-06-10## [1] "Summary before imputation: "
## PubDate.year.fctr PubDate.date.fctr PubDate.wkday.fctr PubDate.wkend
## 2014:8402 (0.97,7]:1981 0: 378 Min. :0.0000
## (7,13] :1757 1:1605 1st Qu.:0.0000
## (13,19] :1808 2:1559 Median :0.0000
## (19,25] :1650 3:1614 Mean :0.0926
## (25,31] :1206 4:1539 3rd Qu.:0.0000
## 5:1470 Max. :1.0000
## 6: 237
## PubDate.hour.fctr PubDate.minute.fctr PubDate.second.fctr
## (-0.023,7.67]:1610 (-0.059,14.8]:3119 (-0.059,14.8]:2134
## (7.67,15.3] :4484 (14.8,29.5] :1671 (14.8,29.5] :2063
## (15.3,23] :2308 (29.5,44.2] :1995 (29.5,44.2] :2112
## (44.2,59.1] :1617 (44.2,59.1] :2093
##
##
##
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.2
## Min. :-0.0274946 Min. :-0.008759
## 1st Qu.:-0.0078858 1st Qu.:-0.007794
## Median : 0.0002845 Median :-0.003965
## Mean : 0.0000000 Mean : 0.000000
## 3rd Qu.: 0.0080100 3rd Qu.: 0.002979
## Max. : 0.0247592 Max. : 0.042684
##
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.4
## Min. :-0.045125 Min. :-0.0183274
## 1st Qu.:-0.007780 1st Qu.:-0.0085001
## Median :-0.000337 Median : 0.0007244
## Mean : 0.000000 Mean : 0.0000000
## 3rd Qu.: 0.007554 3rd Qu.: 0.0081632
## Max. : 0.052153 Max. : 0.0667744
##
## PubDate.day.minutes.poly.5 PubDate.last1.log PubDate.last10.log
## Min. :-0.0245092 Min. : 0.000 Min. : 0.000
## 1st Qu.:-0.0082017 1st Qu.: 5.263 1st Qu.: 8.516
## Median :-0.0003821 Median : 6.292 Median : 8.868
## Mean : 0.0000000 Mean : 6.094 Mean : 9.048
## 3rd Qu.: 0.0077006 3rd Qu.: 7.126 3rd Qu.: 9.424
## Max. : 0.0847176 Max. :10.875 Max. :11.744
##
## PubDate.last100.log .rnorm WordCount.log
## Min. : 0.00 Min. :-3.281785 Min. :0.6932
## 1st Qu.:11.37 1st Qu.:-0.681275 1st Qu.:5.2679
## Median :11.43 Median : 0.007735 Median :5.9480
## Mean :11.49 Mean :-0.000264 Mean :5.8263
## 3rd Qu.:11.78 3rd Qu.: 0.673409 3rd Qu.:6.6067
## Max. :12.95 Max. : 3.987726 Max. :9.2977
## NA's :109
## myCategory.fctr
## ## :1626
## Business#Business Day#Dealbook:1256
## Culture#Arts# : 920
## TStyle## : 829
## OpEd#Opinion# : 690
## Business#Technology# : 453
## (Other) :2628
##
## iter imp variable
## 1 1 WordCount.log
## 1 2 WordCount.log
## 1 3 WordCount.log
## 1 4 WordCount.log
## 1 5 WordCount.log
## 2 1 WordCount.log
## 2 2 WordCount.log
## 2 3 WordCount.log
## 2 4 WordCount.log
## 2 5 WordCount.log
## 3 1 WordCount.log
## 3 2 WordCount.log
## 3 3 WordCount.log
## 3 4 WordCount.log
## 3 5 WordCount.log
## 4 1 WordCount.log
## 4 2 WordCount.log
## 4 3 WordCount.log
## 4 4 WordCount.log
## 4 5 WordCount.log
## 5 1 WordCount.log
## 5 2 WordCount.log
## 5 3 WordCount.log
## 5 4 WordCount.log
## 5 5 WordCount.log
## PubDate.year.fctr PubDate.date.fctr PubDate.wkday.fctr PubDate.wkend
## 2014:8402 (0.97,7]:1981 0: 378 Min. :0.0000
## (7,13] :1757 1:1605 1st Qu.:0.0000
## (13,19] :1808 2:1559 Median :0.0000
## (19,25] :1650 3:1614 Mean :0.0926
## (25,31] :1206 4:1539 3rd Qu.:0.0000
## 5:1470 Max. :1.0000
## 6: 237
## PubDate.hour.fctr PubDate.minute.fctr PubDate.second.fctr
## (-0.023,7.67]:1610 (-0.059,14.8]:3119 (-0.059,14.8]:2134
## (7.67,15.3] :4484 (14.8,29.5] :1671 (14.8,29.5] :2063
## (15.3,23] :2308 (29.5,44.2] :1995 (29.5,44.2] :2112
## (44.2,59.1] :1617 (44.2,59.1] :2093
##
##
##
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.2
## Min. :-0.0274946 Min. :-0.008759
## 1st Qu.:-0.0078858 1st Qu.:-0.007794
## Median : 0.0002845 Median :-0.003965
## Mean : 0.0000000 Mean : 0.000000
## 3rd Qu.: 0.0080100 3rd Qu.: 0.002979
## Max. : 0.0247592 Max. : 0.042684
##
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.4
## Min. :-0.045125 Min. :-0.0183274
## 1st Qu.:-0.007780 1st Qu.:-0.0085001
## Median :-0.000337 Median : 0.0007244
## Mean : 0.000000 Mean : 0.0000000
## 3rd Qu.: 0.007554 3rd Qu.: 0.0081632
## Max. : 0.052153 Max. : 0.0667744
##
## PubDate.day.minutes.poly.5 PubDate.last1.log PubDate.last10.log
## Min. :-0.0245092 Min. : 0.000 Min. : 0.000
## 1st Qu.:-0.0082017 1st Qu.: 5.263 1st Qu.: 8.516
## Median :-0.0003821 Median : 6.292 Median : 8.868
## Mean : 0.0000000 Mean : 6.094 Mean : 9.048
## 3rd Qu.: 0.0077006 3rd Qu.: 7.126 3rd Qu.: 9.424
## Max. : 0.0847176 Max. :10.875 Max. :11.744
##
## PubDate.last100.log .rnorm WordCount.log
## Min. : 0.00 Min. :-3.281785 Min. :0.6931
## 1st Qu.:11.37 1st Qu.:-0.681275 1st Qu.:5.2691
## Median :11.43 Median : 0.007735 Median :5.9480
## Mean :11.49 Mean :-0.000264 Mean :5.8262
## 3rd Qu.:11.78 3rd Qu.: 0.673409 3rd Qu.:6.6067
## Max. :12.95 Max. : 3.987726 Max. :9.2977
##
## myCategory.fctr
## ## :1626
## Business#Business Day#Dealbook:1256
## Culture#Arts# : 920
## TStyle## : 829
## OpEd#Opinion# : 690
## Business#Technology# : 453
## (Other) :2628mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## Popular Popular.fctr WordCount.log
## 1870 1870 109
## [1] "numeric data w/ 0s in : "
## WordCount Popular PubDate.wkday.fctr
## 109 5439 378
## PubDate.wkend PubDate.last1 PubDate.last1.log
## 7624 11 11
## PubDate.last10 PubDate.last10.log PubDate.last100
## 10 10 100
## PubDate.last100.log PubDate.wkday.fctr.nonNA
## 100 378
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate myCategory
## 17 0 0glb_chunks_df <- myadd_chunk(glb_chunks_df, "encode.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 4 manage.missing.data 2 2 37.920 46.451 8.531
## 5 encode.data 2 3 46.451 NA NAStep 2.3: encode data
# map_<col_name>_df <- myimport_data(
# url="<map_url>",
# comment="map_<col_name>_df", print_diagn=TRUE)
# map_<col_name>_df <- read.csv(paste0(getwd(), "/data/<file_name>.csv"), strip.white=TRUE)
# glb_trnobs_df <- mymap_codes(glb_trnobs_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_newobs_df <- mymap_codes(glb_newobs_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_trnobs_df$<col_name>.fctr <- factor(glb_trnobs_df$<col_name>,
# as.factor(union(glb_trnobs_df$<col_name>, glb_newobs_df$<col_name>)))
# glb_newobs_df$<col_name>.fctr <- factor(glb_newobs_df$<col_name>,
# as.factor(union(glb_trnobs_df$<col_name>, glb_newobs_df$<col_name>)))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 encode.data 2 3 46.451 46.478 0.027
## 6 extract.features 3 0 46.478 NA NAStep 3.0: extract features
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
extract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 46.529 NA NA# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 46.529 46.539
## 2 extract.features_factorize.str.vars 2 0 46.540 NA
## elapsed
## 1 0.011
## 2 NAprint(str_vars <- myfind_chr_cols_df(glb_allobs_df))## NewsDesk SectionName SubsectionName Headline
## "NewsDesk" "SectionName" "SubsectionName" "Headline"
## Snippet Abstract PubDate .src
## "Snippet" "Abstract" "PubDate" ".src"
## myCategory
## "myCategory"if (length(str_vars <- setdiff(str_vars,
glb_exclude_vars_as_features)) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <- factor(glb_allobs_df[, var],
as.factor(unique(glb_allobs_df[, var])))
# glb_trnobs_df[, paste0(var, ".fctr")] <- factor(glb_trnobs_df[, var],
# as.factor(unique(glb_allobs_df[, var])))
# glb_newobs_df[, paste0(var, ".fctr")] <- factor(glb_newobs_df[, var],
# as.factor(unique(glb_allobs_df[, var])))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}
if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(re_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(re_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
#tmp_freq_df <- chk_pattern_freq("\\bNew (\\w)+", ignore.case=FALSE)
#subset(chk_pattern_freq("\\bNew (\\w)+", ignore.case=FALSE), grepl("New [[:upper:]]", pattern))
#chk_pattern_freq("\\bnew (\\W)+")
chk_subfn <- function(pos_ix) {
re_str <- gsubfn_args_lst[["re_str"]][[pos_ix]]
print("re_str:"); print(re_str)
rp_frmla <- gsubfn_args_lst[["rp_frmla"]][[pos_ix]]
print("rp_frmla:"); print(rp_frmla, showEnv=FALSE)
tmp_vctr <- grep(re_str, txt_vctr, value=TRUE, ignore.case=TRUE)[1:5]
print("Before:")
print(tmp_vctr)
print("After:")
print(gsubfn(re_str, rp_frmla, tmp_vctr, ignore.case=TRUE))
}
#chk_subfn(1)
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end], glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#all.equal(sav_txt_lst[["Headline"]][1:2000], glb_txt_lst[["Headline"]][1:2000])
#all.equal(sav_txt_lst[["Headline"]][1:1000], glb_txt_lst[["Headline"]][1:1000])
#all.equal(sav_txt_lst[["Headline"]][1:500], glb_txt_lst[["Headline"]][1:500])
#all.equal(sav_txt_lst[["Headline"]][1:200], glb_txt_lst[["Headline"]][1:200])
#all.equal(sav_txt_lst[["Headline"]][1:100], glb_txt_lst[["Headline"]][1:100])
#chk.equal( 1, 100)
#chk.equal(51, 100)
#chk.equal(81, 100)
#chk.equal(81, 90)
#chk.equal(81, 85)
#chk.equal(86, 90)
#chk.equal(96, 100)
#dsp.equal(86, 90)
glb_txt_map_df <- read.csv("mytxt_map.csv", comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(tmp_vctr <- grep("[[:upper:]]\\.", txt_vctr, value=TRUE, ignore.case=FALSE))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, tolower)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation,
preserve_intra_word_dashes=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
filter_df <- read.csv("mytxt_compound.csv", comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, tolower) #nuppr
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
# Not to be run in production
inspect_terms <- function() {
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full +term, full_Tf_df)
print(myplot_histogram(full_Tf_df, "Tf.full"))
myprint_df(full_Tf_df)
#txt_corpus[[which(grepl("zun", txt_vctr, ignore.case=TRUE))]]
digit_terms_df <- subset(full_Tf_df, grepl("[[:digit:]]", term))
myprint_df(digit_terms_df)
return(full_Tf_df)
}
#print("RemovePunct:"); remove_punct_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english"))) #nstopwrds
#print("StoppedWords:"); stopped_words_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, stemDocument) #Features for lost information: Difference/ratio in density of full_TfIdf_DTM ???
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_Tf_df <- inspect_terms()
#stemmed_stopped_Tf_df <- merge(stemmed_words_Tf_df, stopped_words_Tf_df, by="term", all=TRUE, suffixes=c(".stem", ".stop"))
#myprint_df(stemmed_stopped_Tf_df)
#print(subset(stemmed_stopped_Tf_df, grepl("compan", term)))
#glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
#grep("scene", names(full_TfIdf_vctr), value=TRUE)
#which.max(full_TfIdf_mtrx[, "yearlong"])
full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
names(full_TfIdf_df) <- "TfIdf.full"
full_TfIdf_df$term <- rownames(full_TfIdf_df)
full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
names(sprs_TfIdf_df) <- "TfIdf.sprs"
sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
# plt_X_df <- cbind(txt_X_df, glb_allobs_df[, c(glb_id_vars, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today", xcol_name=glb_rsp_var))
# log_X_df <- log(1 + txt_X_df)
# colnames(log_X_df) <- paste(colnames(txt_X_df), ".log", sep="")
# plt_X_df <- cbind(log_X_df, glb_allobs_df[, c(glb_id_vars, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today.log", xcol_name=glb_rsp_var))
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#sav_allobs_df <- glb_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_vars, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
#txt_X_df <- glb_allobs_df[, c(glb_id_vars, ".rnorm"), FALSE]
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create user-specified pattern vectors
# <txt_var>.P.year.colon
txt_X_df[, paste0(txt_var_pfx, ".P.year.colon")] <-
as.integer(0 + mycount_pattern_occ("[0-9]{4}:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.daily.clip.report")] <-
as.integer(0 + mycount_pattern_occ("Daily Clip Report", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.fashion.week")] <-
as.integer(0 + mycount_pattern_occ("Fashion Week", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.first.draft")] <-
as.integer(0 + mycount_pattern_occ("First Draft", glb_allobs_df[, txt_var]))
#sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
if (txt_var %in% c("Snippet", "Abstract")) {
txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
glb_allobs_df[, txt_var]))
}
#sum(mycount_pattern_occ("[0-9]{4}:", glb_allobs_df$Headline) > 0)
#sum(mycount_pattern_occ("Quandary(.*)(?=:)", glb_allobs_df$Headline, perl=TRUE) > 0)
#sum(mycount_pattern_occ("No Comment(.*):", glb_allobs_df$Headline) > 0)
#sum(mycount_pattern_occ("Friday Night Music:", glb_allobs_df$Headline) > 0)
if (txt_var %in% c("Headline")) {
txt_X_df[, paste0(txt_var_pfx, ".P.facts.figures")] <-
as.integer(0 + mycount_pattern_occ("Facts & Figures:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.friday.night.music")] <-
as.integer(0 + mycount_pattern_occ("Friday Night Music", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.no.comment.colon")] <-
as.integer(0 + mycount_pattern_occ("No Comment(.*):", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.on.this.day")] <-
as.integer(0 + mycount_pattern_occ("On This Day", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.quandary")] <-
as.integer(0 + mycount_pattern_occ("Quandary(.*)(?=:)", glb_allobs_df[, txt_var], perl=TRUE))
txt_X_df[, paste0(txt_var_pfx, ".P.readers.respond")] <-
as.integer(0 + mycount_pattern_occ("Readers Respond", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.recap.colon")] <-
as.integer(0 + mycount_pattern_occ("Recap:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.s.notebook")] <-
as.integer(0 + mycount_pattern_occ("s Notebook", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.today.in.politic")] <-
as.integer(0 + mycount_pattern_occ("Today in Politic", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.today.in.smallbusiness")] <-
as.integer(0 + mycount_pattern_occ("Today in Small Business:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.verbatim.colon")] <-
as.integer(0 + mycount_pattern_occ("Verbatim:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.what.we.are")] <-
as.integer(0 + mycount_pattern_occ("What We're", glb_allobs_df[, txt_var]))
}
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_vars, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
rm(log_X_df, txt_X_df)
}## Loading required package: stringr
## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 46.540 47.281
## 3 extract.features_process.text 3 0 47.282 NA
## elapsed
## 2 0.741
## 3 NA
## [1] "Building glb_txt_lst..."
## [1] "Remaining Acronyms in Headline:"
## character(0)
## [1] "Remaining Acronyms in Snippet:"
## [1] "In the 1864 election, Gotham delivered nearly twice as many votes to the presidents opponent, George B. McClellan."
## [2] "Researchers are finding more evidence that women who take S.S.R.I. depressants like Prozac and Zoloft increase the likelihood of a variety of health problems in their newborns."
## [3] "Eric T. Schneiderman, NewYorks attorney general, filed a suit on Tuesday that accuses Evans Bank of denying mortgages to African-Americans in Buffalo regardless of their credit."
## [4] "Jeffrey H. Knox, a senior federal prosecutor who butted heads with a number of WallStreet banks, is switching sides."
## [5] "In which Peter A. Collins disproves Aesop."
## [6] "Antonia M. Apps, the lead federal prosecutor in the criminal trial last year of Michael Steinberg, is taking a job with the law firm Milbank, Tweed, Hadley & McCloy."
## [7] "Scholarly musicians prove that Ph.D.s can play."
## [8] "In this new series, Chris Labzda and Bon Duke, the co-founders of the NewYork Fashion Film Festival, curate a short film each week for T. This weeks installment: A collage of classics overlaid with sound from Lauren Wolksteins Social..."
## [9] "I moved to Hudson, N.Y., to slow down and save money. I didnt know Id feel so lonely."
## [10] "A study suggests that exercise can help kids, especially those with A.D.H.D., focus in class."
## [11] "How should the USA respond to the killings of Steven J. Sotloff and James Foley?"
## [12] "Although banks are hiring armies of legal and compliance professionals in response to settlements and fines, they would do better to find ways to streamline their existing legal work, Geoffrey A. Moore and Mark Harris write in an Another View column."
## [13] "William T. Shermans 1864 campaign to take the Georgia city was one of the bloodiest of the Civil War."
## [14] "George Martin, author of the Song of Ice and Fire series, will be promoting his new book at the 92nd St. Y."
## [15] "The workers are OK."
## [16] "The Securities and Exchange Commission has chosen Tracey L. McNeil, an agency veteran and former corporate lawyer, to act as a liaison in resolving problems that retail investors may have with the agency."
## [17] "John A. Paulsons hedge fund suffered across-the-board losses in July and did only marginally better in August, according to an update to investors."
## [18] "Mr. Sheen will direct and perform in Dylan Thomass work he called it a play for voices on Oct. 26 at the 92nd Street Y."
## [19] "The acquisition is the latest move by Jeffrey R. Immelt, the chief of GE, to refocus the conglomerate on its core industrial businesses."
## [20] "The Chinese state news media has cited concerns that the USA is indoctrinating Chinese students by including its founding documents upholding freedom and human rights in the SAT."
## [21] "At the midpoint of the range, the Citizens Financial Group, based in Providence, R.I., would be valued at $13.4 billion."
## [22] "Mark P. Frissoras decision comes a few weeks after Carl C. Icahn disclosed an 8.48 percent stake in the rental car provider."
## [23] "Martin Lipton is virtually guaranteeing the continuation of policies opposed by many NYU faculty members, William D. Cohan writes in the Street Scene column."
## [24] "China Central Television used images of Condoleezza Rice in a report about a Beijing visit by Susan E. Rice, President Obamas national security adviser."
## [25] "If youve never heard the name Lloyd J. Austin, theres a good reason"
## [26] "This weeks video features Ismenia Mendes and David McElwee in a scene from A. R. Gurneys 1977 drama The Wayside Motor Inn."
## [27] "The media and financial data company Thomson Reuters is looking to sell peHUB, Buyouts and Venture Capital Journal, according to a report by peHUB."
## [28] "News headlines briefly flashed that Carl C. Icahn had raised his stake in Gannett, the media company, to nearly 9 percent. But as it turns out, his firm simply made a typo in a regulatory filing."
## [29] "Just two days after her USA Open victory, Serena Williams hosted her first ever fashion show for HSN."
## [30] "Nearly 70 years after they parted, a Chinese veteran is on a quest to find a Japanese woman he met shortly after World War II. His tale offers a warmer recollection of that era."
## [31] "In this lesson we offer a series of topics and questions paired with Times essays, articles, slide shows and videos to help students dig deeper into the causes, effects and overall legacy of World War I."
## [32] "A collection of material related to Rosa Parks, bought last month by Howard G. Buffett, is being lent to the Library of Congress for 10 years."
## [33] "A soldier takes time out of uniform to pursue an M.F.A. in writing, and discovers that what sets him apart, as a veteran at the keyboard, is not as important as the common ground he shares with other writers."
## [34] "Worn by the chef from July 29 to Aug. 4 while cooking at his two restaurants, Blue Hill in Manhattan and Blue Hill at Stone Barns in Pocantico Hills, N.Y."
## [35] "Gov. Andrew M. Cuomo and Mayor Bill de Blasio both enjoyed an Italian sausage at the Feast of SanGennaro in Little Italy on Saturday."
## [36] "Eike Batista was accused of manipulating the share price of his now-bankrupt petroleum company OGX."
## [37] "The activist investor William A. Ackmans new fund, Pershing Square Holdings, aims to list on the the Euronext Amsterdam exchange on Oct. 13."
## [38] "Fraud in the market for penny stocks continues unabated, two criminal cases filed last week show, Peter J. Henning writes in the White Collar Watch column."
## [39] "Quarries and connecting trenches were nothing less than small cities, where the walls bear witness to the people who fought and died in World War I."
## [40] "The deal for Vkontakte ends tensions between the social networks minority shareholders and Alisher B. Usmanov, the Russian billionaire who controls Mail.ru."
## [41] "Sears is borrowing $400 million from its chief, the billionaire Edward S. Lampert, through his hedge fund."
## [42] "KKRs purchase of Pioneers D.J. audio equipment business is the modern-day equivalent of providing gold-rush pickaxes to participants in the rave rush, Jeffrey Goldfarb writes in Reuters Breakingviews."
## [43] "Two grandchildren of Dwight D. Eisenhower say proposed changes by Frank Gehry to his memorial to the former president do not satisfy their concerns."
## [44] "In a letter to Electra Private Equity shareholders, the activist investor Edward J. Bramson said a change in approach in Electras strategy could increase the value of its shares by about $1.6 billion."
## [45] "Prosecutors in Brazil have argued that Mr. Batista profited from insider information when he sold shares in his now bankrupt petroleum company, OGX."
## [46] "Comments by Marshall L. Miller, the No. 2 official in the Justice Departments criminal division, reflect the agencys renewed interest in charging individual bank employees rather than just the banks."
## [47] "The top job at the database giant will be shared by Mark V. Hurd, now co-president, and Safra Catz, who is co-president and chief financial officer."
## [48] "Alibaba is set to start trading on the NewYork Stock Exchange under the ticker symbol BABA. | Markets showed relief that the United Kingdom would not face a tumultuous breakup. | Lawrence J. Ellison announced his retirement as chief executive of..."
## [49] "Average waist circumference but not B.M.I. has increased significantly in the USA, a new study reports."
## [50] "The British private equity firm accused Edward J. Bramsons Sherborne Investors Management of making unverifiable statements and unsubstantiated claims in its effort to reshape Electra."
## [51] "Alibaba soared in its public market debut. | Siemens agreed to buy the Dresser-Rand Group. | EMC weighed a deal with H.P. | Public pension funds may be souring on hedge funds."
## [52] "Venezuela may represent the Latin American and Caribbean nations and sit right next to the USA."
## [53] "Speaker John A. Boehner seemed to blame lazy Americans for the stalled economy."
## [54] "It was unclear whether President Petro O. Poroshenko would travel to NewYork to deliver his address as scheduled on Thursday."
## [55] "As the reclusive genius Richard D. James releases his first album in 13 years, the graphic designer with whom he has frequently collaborated discusses their work together."
## [56] "Vice President Joseph R. Biden Jr. met Carina Castro, the daughter of Julin Castro, secretary of Housing and Urban Development, at a reception honoring Hispanic Heritage Month."
## [57] "Attorney General Eric H. Holder Jr. will announce Tuesday that the federal government will end its fiscal year next week with 4,800 fewer prisoners, the first time since 1980 that the inmate population has declined from year to year."
## [58] "President Obama could end up leaving a smaller imprint on the judiciary than either President Bill Clinton or President George W. Bush."
## [59] "Ever wonder why jewelry stores hide the price tags? How invoice factoring works for small businesses. And why German companies are on a buying spree in the USA."
## [60] "Speaker John A. Boehner urges the new Veterans Affairs secretary to look to the private sector in reviewing how the department delivers health care to veterans."
## [61] "On this day in 1952, Richard M. Nixon gave his famous Checkers speech."
## [62] "To kick off Paris Fashion Week, the chic native whose shirting-based collection is stocked at Barneys NewYork and Dover Street Market shares a list of her favorite places around town with T."
## [63] "Gov. Paul R. LePage of Maine wants to help the NFL address its domestic violence problem."
## [64] "Maurice R. Greenberg, the former chief of AIG, has now raised several million dollars from three WallStreet investors to help cover the cost of the case."
## [65] "Electra said that two influential shareholder advisory services opposed efforts of the activist investor Edward J. Bramson, through his Sherborne Investors Management, to shake up the board of the British private equity firm."
## [66] "Representative James A. Traficant was seriously injured after a tractor accident on his family farm Tuesday night, according to local media reports in Youngstown, Ohio."
## [67] "The role of former President Gerald R. Ford in the filing of a friend-of-the-court brief in a Michigan affirmative action case."
## [68] "The insider trading case against Michael A. Lucarelli, a former executive at Lippert/Heilshorn and Associates, was relatively minor. But he got attention for running barefoot from the court in August."
## [69] "Judge Richard M. Berman of Federal District Court in Manhattan, before sentencing conservative author Dinesh DSouza to probation for violating federal campaign finance laws."
## [70] "Speaker John A. Boehner, an avid golfer, is gearing up for the Ryder Cup, the intense competition between the USA and Europe that begins Friday in Scotland."
## [71] "Speaker John A. Boehner tells First Draft that the new Congress should debate military action in Syria."
## [72] "A new poll has Senator Mark R. Warner, the Democratic incumbent in Virginia, up by 9 points over his Republican opponent."
## [73] "Eric H. Holder Jr.s resignation as attorney general means that President Obama is losing one the longest-serving members of his cabinet and one of his closest confidants."
## [74] "The Department of Labor is putting money behind its push to expand paid family leave in the USA. The issue could be a factor in coming elections if enough voters ask about it."
## [75] "Members of the NewYork Police Department turned out in force on Thursday for the funeral of Officer Michael Williams in LaGrangeville, N.Y."
## [76] "How big a fight will President Obama put up over Attorney General Eric H. Holder Jr.s successor? The question is captivating Washington."
## [77] "Eric H. Holder Jr. has resigned as attorney general, but hes in no hurry to go anywhere."
## [78] "William H. Gross, who built Pimco into one of the largest asset managers in the world, will join Janus Capital after a decision had been made for him to leave Pimco or be forced out, said a person briefed on the matter."
## [79] "William H. Gross, for decades an investment guru, was undone by the increasing complexity of managing his ever-growing fund."
## [80] "Two of Allergans largest shareholders, T. Rowe Price and Pentwater Capital Management, have broken their silence to insist that the company not strike any deals before a scheduled special meeting in December."
## [81] "As Yahoo tries to figure out what to do after raising $6 billion from its stake in the Alibaba Group, a prominent investor, Starboard Value, has emerged to offer some suggestions including buying AOL."
## [82] "Speaker John A. Boehner says Republicans must compete in Northeast"
## [83] "The ruling by Judge Thomas P. Griesa of the Federal District Court in Manhattan allows Citigroup to make a $5 million payment to bondholders."
## [84] "Remembering James A. Trafficant, in videos and soundbites."
## [85] "The low-power computer server that has been seen as a way to revive Hewlett-Packard has never lived up to expectations. Still, HP keeps trying, this time with a chip made by ARM."
## [86] "William H. Gross abruptly leaves Pimco. | AIG case sheds new light on bailout. | DreamWorks Animation in sale talks with SoftBank. | Allergan shareholders speak out."
## [87] "Judge Thomas P. Griesa of Federal District Court in Manhattan stopped short of issuing sanctions, saying he would make a decision about them in the future."
## [88] "A suit filed by Stephen A. Wynn, the casino impresario, against an investor he accuses of slander is the latest salvo in the growing tensions between vocal shareholders and corporations."
## [89] "Kyle T. Dolan spins us a puzzle."
## [90] "The move, which the activist investor Carl C. Icahn had called for, will cleave eBay almost in half and separate it from a company that generates almost half its revenue."
## [91] "The team behind the popular Lower East Side restaurant are publishing a collection of 100 seasonal dishes meant to provide both information and inspiration. Here, they share a recipe with T."
## [92] "Elizabeth C. Gorski gives us the lowdown."
## [93] "The British media and marketing firms acquisition of Advanstar, a private company, would create the largest events organizer in the USA."
## [94] "The initial public offering of Pershing Square Holdings is expected to give the activist investor William A. Ackman a permanent pool of capital to make bigger, bolder bets."
## [95] "James R. Clapper Jr., the director of national intelligence, sent a message to employees on Tuesday defending the nations spy agencies against criticism."
## [96] "David A. ONeil has not announced his next step, though he will probably swing through Washingtons revolving door."
## [97] "A Hungarian musicologist finds Mozarts own score of the Piano Sonata in A, K. 331."
## [98] "After 20 years on the board of the Alvin Ailey Dance Foundation, Joan H. Weill, its chairwoman, is expected to announce Thursday that she is stepping down at the end of the year."
## [99] "Several recent bankruptcy cases and news events have shown that municipal and corporate debt securities have faced issues in pricing and trading, Stephen J. Lubben writes in an In Debt column."
## [100] "The countrys largest banks are rolling out a new service that could help them lessen their reliance on expensive data terminals like those sold by Bloomberg L.P."
## [101] "What White House security breaches happened during the presidencies of Franklin D. Roosevelt, Herbert Hoover and Ronald Reagan?"
## [102] "Politicians and NewYorks elite gathered Wednesday for the Alfred E. Smith charity dinner and roast."
## [103] "George W. Bush talks about his brother Jebs presidential aspirations."
## [104] "Im interested in buying a fairy house, I told the first branch of the phone tree at Bank of America Home Loans. In August, the company agreed to pay a $16.65 billion penalty for its role in selling bonds based on make-believe mortgages. I..."
## [105] "Michael R. Bloomberg and two Disney princesses."
## [106] "Former President George W. Bush said on Thursday that he had been lobbying his younger brother Jeb to throw his hat in the ring in 2016"
## [107] "The government said the criminal charges against Michael J. Coscia, founder of Panther Energy Trading, were the first to be brought under new rules that bar a type of abusive trading called spoofing."
## [108] "The Solomon R. Guggenheim Museum is planning the construction of a new building somewhere in NewYork City that will be used for offices, art storage and some public programming."
## [109] "Speaker John A. Boehner has picked Elise Stefanik, who is running for an open House seat in upstate NewYork, to deliver the partys radio address on Saturday."
## [110] "As the billionaire Kenneth C. Griffin battles his wife in divorce court, he has drawn back the curtain on one of the most prominent marriages in hedge fund world by disclosing terms of their prenuptial agreement."
## [111] "Jesse C. Litvak had been one of the few people convicted of fraud over the bailout of WallStreet, but an appeals court suggested that his conviction was likely to be overturned."
## [112] "Before my son started school, I shared my parents hippie view of the P.T.A. as a perfect-parent filled hassle and just another part of The Establishment trying to squelch creativity out of learning."
## [113] "Vice President Joseph R. Biden Jr. held a question and answer session at Harvard on Thursday."
## [114] "The investor Edward J. Bramson had been pushing to shake up the board of the British private equity firm Electra Private Equity and had sought two seats on its board."
## [115] "Including hard terms for AIG was politically necessary to getting the TARP program going, but Henry M. Paulson Jr. said he did support the bailout package and its terms."
## [116] "These D.I.Y. magazines are on the rise in LosAngeles, where artists are using them as a showcase for their work."
## [117] "A growing body of research indicates that many people who react to gluten may be suffering a condition called non-celiac gluten sensitivity, or NCGS."
## [118] "A big hurdle in the spoofing case against a high-frequency trading firm is that a jury must decide whether one computer fooling another is a crime, Peter J. Henning writes in the White Collar Watch column."
## [119] "Just 24 percent of respondents to a Pew Research Center poll correctly picked Janet L. Yellen as the Feds chairwoman from a list of four names."
## [120] "Maurice R. Greenberg, AIGs former chief and a large shareholder, has spun a ludicrous tale in court that the bailout of the insurer was unfair to its investors."
## [121] "Duncan L. Niederauer, who recently retired as head of the NewYork Stock Exchange, has joined an upstart brokerage firm called Battery East, which aims to help employees of privately held companies sell their shares."
## [122] "Big banks face another round of USA charges. | Henry M. Paulson Jr. testifies that punitive AIG terms were necessary. | Investors cheer the breakup of HP. | Lawyers for Goldman and Libyas sovereign fund clash in court."
## [123] "Timothy F. Geithner, the former Treasury secretary, was a witness in the trial of a lawsuit over the departments role in the bailout of AIG His book was under scrutiny, too."
## [124] "By chronicling the building blocks of technological advances, Walter Isaacsons The Innovators hopes to teach us about the nature of innovation, Jonathan A. Knee writes in a review, the debut Book Entry column."
## [125] "President Obama continues to get low marks on his handling of the threat from the Islamic State, also known as ISIS or ISIL."
## [126] "Valeant and Pershing Square are planning to raise their offer for Allergan. | Profit at Goldman less easy to find. | AIG trial puts Timothy F. Geithner on the hot seat. | Glencores chief pursues Rio Tinto."
## [127] "A derivatives rule is set to change. | JPMorgans data breach causes alarm. | Chinese companies are scooping up real estate across the globe. | Former Treasury Secretary Timothy F. Geithner defends the bailout of AIG"
## [128] "The $2.6 billion deal, which includes debt, is the latest transaction to reshape the drug industry. Auxilium is terminating its deal to acquire QLT."
## [129] "Carl C. Icahn, the billionaire activist investor, sent a letter to Timothy Cook, the chief executive of Apple, saying that the company is hugely undervalued."
## [130] "Critics want a war memorial to remove a sculpture based on Alfred Eisenstaedts famous Life Magazine photo of a sailor kissing a nurse at the end of World War II."
## [131] "One way to level the playing field is to simply repeal the safe harbors in the bankruptcy code, writes Stephen J. Lubben in the In Debt column."
## [132] "Facebook has a new local mobile advertising play. J.C. Penney is in the midst of an e-commerce renaissance."
## [133] "The AM1 Supreme by MARCH LA.B bears the hallmarks of vintage watches, but with a streamlined aesthetic."
## [134] "The government acknowledged there was scant legal precedent for its demand for $1.6 million in restitution from Jesse C. Litvak, who was convicted of securities fraud in March."
## [135] "Ben S. Bernanke, the former Federal Reserve chairman, kept his answers brief on the stand in the lawsuit over the 2008 bailout of American International Group."
## [136] "A sluggish global outlook sends ripples through the markets. | Tuning up UBSs investment bank. | Symantec announces a split. | Ben S. Bernanke defends the AIG bailout."
## [137] "The business will merge with a fledgling firm founded by Paul J. Taubman, the former Morgan Stanley investment banker, who will run the combined firm as chief executive and chairman."
## [138] "If you want to time-travel to the 1990s, youll get a chance at 1 p.m. today, when the William J. Clinton Library releases 10,000 more pages of previously undisclosed documents from the Clinton years."
## [139] "Field + Supply, founded by the interior designer Brad Ford, launches this weekend in a barn in High Falls, N.Y. Here, a look at some of the furniture and objects on offer."
## [140] "Ben Ratliff and Jon Caramanica discuss the singers Tinashe and FKA twigs, and whether they point toward a new conception of R&B."
## [141] "The Army War College rescinded the masters degree of Senator John E. Walsh on Friday, determining that the Montana Democrat plagiarized his final paper there in 2007."
## [142] "A restaurant learns what happens when you tell customers to pay what God wants them to pay, Microsofts chief executive says its O.K. for women to ask for a raise, and new restaurants are making Detroit a culinary oasis."
## [143] "A new USAO. lounge for service members and their families has opened at Terminal 5 at Kennedy Airport in NewYork."
## [144] "Vice President Spiro T. Agnew resigned on this day in 1973."
## [145] "Holly Fallon, whose stage name is Dollicious, and Miriam Hintz, who goes by Haru, posed in the lower courtyard of the Jacob K. Javits Convention Center on Friday at NewYork Comic Con."
## [146] "Two cases raise the question about what role the courts should play in policing negotiations and the limits that can be applied to the tactics one side can use in making a deal, Peter J. Henning writes in the White Collar Watch column."
## [147] "The awards will be announced on Nov. 23 in a show broadcast by ABC."
## [148] "A 28 year old digital communications director is helping to turn Speaker John A. Boehner, no ones idea of a digital-age guru, into a YouTube star."
## [149] "Abigail Johnson, 52, who is president of the parent company of Fidelity Investments, will succeed her father, Edward C. Johnson III, as chief executive."
## [150] "Photos from HongKong, Syria, Yemen and the USA."
## [151] "Adam G. Perl puns his way through our Tuesday puzzle."
## [152] "JPMorgan Chase posts a third quarter profit. | Warren E. Buffett promotes the Berkshire Hathaway brand. | Calculating the cost of Ebola | Derivatives change only goes so far."
## [153] "Denis J. McInerney, a former deputy assistant attorney general in the Justice Department, has returned to the law firm Davis Polk & Wardwell."
## [154] "President Obama has decided to wait until after next months midterm elections to nominate a replacement for Attorney General Eric H. Holder Jr., White House officials said."
## [155] "Some of it is OK."
## [156] "Highlights from the International Herald Tribune archives: Nikita S. Khrushchev was believed to be ousted as the Soviet leader in 1964."
## [157] "Hopper Drawing, by Carter E. Foster, was published by the Whitney Museum of American Art in connection with a Hopper exhibition in 2013."
## [158] "Airbnb, the pioneering home rental service, presents itself as useful and virtuous, but the reality is far less benign, according to Attorney General Eric T. Schneiderman of NewYork"
## [159] "The FBI director, James B. Comey, corrected statements he made on 60 Minutes that the bureau did not do electronic surveillance without a court order."
## [160] "Ben C. Solomon is a Times video journalist reporting on Ebola. His video today, about a team of ambulance drivers in Monrovia, Liberia, shows the dangers they face every day."
## [161] "James L. Amine and Timothy P. OHara have been appointed to the Swiss lenders executive board and will lead the investment banking division with Gal de Boissard, the chief executive for Europe, the Middle East and Africa."
## [162] "Food news from Silicon Valley to the UK."
## [163] "The venture capitalist Marc Andreessen is stepping down from eBays board, months after publicly defending his role as a director against attacks from Carl C. Icahn."
## [164] "Best known for his operas and symphonies, Mr. Glasss Piano tudes are the focus of a new recording and concerts at BAM."
## [165] "An SEC case against a high-frequency trading firm shows how difficult it is to draw the line between acceptable trading strategies and manipulation, Peter J. Henning writes in the White Collar Watch column."
## [166] "Vice President Joseph R. Biden Jr. appeared in a hangar near La Guardia Airport on Monday to endorse Gov. Andrew M. Cuomos plans for commissioning a redesign of four airports in and around NewYork City."
## [167] "Senator Patrick J. Leahy, chairman of the Judiciary Committee, is asking Comcast not to engage in paid prioritization of Internet content after its proposed takeover of Time Warner Cable."
## [168] "Tom Steyer, a billionaire hedge fund founder, has become the largest super PAC donor of all time, passing the casino magnate Sheldon G. Adelson."
## [169] "The piano superstar plays the solo cadenza for the first movement of Mozarts Piano Concerto No. 17 in G."
## [170] "If you are wondering why you have been getting fund-raising pitches from the Democratic Congressional Campaign Committee theres at least one explanation: an organization you did provide your email to has rented its list to the WashingtonDCC.C."
## [171] "Through a clever bit of photo manipulation, a new ad by the NRA puts Iowas Democratic Senate nominee, Bruce Braley, next to the man many gun owners consider their nemesis: Michael R. Bloomberg."
## [172] "Photos from SouthAfrica, HongKong, India and the USA."
## [173] "Vice President Joseph R. Biden Jr. beat the comics to the punch line at a recent Kennedy Center appearance."
## [174] "A federal judge ordered a full mental evaluation in the next 30 days of Omar J. Gonzalez, the man accused of jumping the fence and racing past Secret Service officers into the White House with a knife last month."
## [175] "Highlights from the International Herald Tribune archives: French farmers disagreed with General de Gaulles threat to quit the E.E.C. in 1964."
## [176] "The Feds policies that drove down interest rates to historically low levels have actually exacerbated the inequality problem that Janet L. Yellen said concerned her, writes William D. Cohan in Street Scene."
## [177] "Ben Bradlees funeral will be held Tuesday at the Washington National Cathedral, where Richard M. Nixon was remembered in a memorial service in 1994."
## [178] "NewYorkTimess David E. Sanger remembers a conversation with Ben Bradlee about an article in NewYorkTimes."
## [179] "At an investment conference, Scott A. Mather, who now oversees Pimcos Total Return Fund, defends its outsize holdings in European government bonds and other high-yielding securities."
## [180] "Children who drink rice, almond or soy milk instead of cows milk may have insufficient levels of vitamin D."
## [181] "A man later identified as a J.P. Morgan managing director walked into a live, online broadcast of a group of prominent protesters on Wednesday to vent his frustration with the continuing occupation of key locations in HongKong."
## [182] "Gov. Andrew M. Cuomo of NewYork foresees something really, really, really big for Hillary Rodham Clintons future."
## [183] "Richard Norton Smiths biography of Nelson Rockefeller is a portrait of the world when establishment financiers earned political respect, writes Jonathan A. Knee in a book review."
## [184] "A senior administration official confirms that Kathyrn Ruemmler, the former White House counsel, has taken herself out of the running to succeed Eric H. Holder Jr. as attorney general."
## [185] "The festival of theater, dance and performance art presented by P.S. 122 will take place at spaces around the city, while its home on First Avenue is renovated."
## [186] "Ben C. Solomon has been making videos about the Ebola outbreak, finding scenes of courage even as people struggle to contain the disease."
## [187] "Most European banks pass stress test. | Steven Ballmer could claim huge tax benefits on his LosAngeles Clippers deal. | William A. Ackmans outsize bets. | Court ruling disarms shareholders."
## [188] "Arguably the greatest feat of arms in American military history was performed on the Roanoke River at Plymouth, N.C., in the predawn darkness of Oct. 28, 1864."
## [189] "One side threatens to crack down harder; the other side complains about too much enforcement. The question is whether both sides can be satisfied, Peter J. Henning writes in the White Collar Watch column."
## [190] "Plus, El Anatsuis shimmering curtains, Martin Z. Marguliess photography collection and more art events in the week ahead."
## [191] "Vice President Joseph R. Biden campaigns for Representative Bruce Braley in Iowa on Monday."
## [192] "D.J. Tim Sweeneys cult-favorite program turns 15 next year. To celebrate, he has compiled a new double album, a song from which premieres here."
## [193] "William J. Burns, who just stepped down as deputy secretary of state, is being named president of the Carnegie Endowment for International Peace."
## [194] "Timothy D. Cook, Apples chief executive, said that one million credit cards had been activated on Apple Pay in the first three days that the mobile payment system was live."
## [195] "The information of more than 18.5 million California residents was compromised in 2013, according to Kamala D. Harris, Californias attorney general."
## [196] "An Apple executive recently called the design of Xiaomis phones theft. In return, Mr. Barra pointed out that parts of Apples new phones are a bit like handsets from HTC."
## [197] "Elizabeth C. Gorski cooks up a funny midweek puzzle for us."
## [198] "Michael E. Shapiro, who transformed and enlarged the collection of Atlantas High Museum, will leave next year."
## [199] "Sales of Gov. Andrew M. Cuomos memoir fell by more than 43 percent to 535 copies in its second week on shelves."
## [200] "Kering announced a new sustainable fashion program and the C.F.D.A. and Lexus announced the winners for their Eco-Fashion challenge."
## [201] "Quinn Bradlee, a son of Benjamin C. Bradlee, the former executive editor of The Washington Post, rested his head on the coffin after he spoke at his fathers funeral at Washington National Cathedral on Wednesday."
## [202] "He had claimed that the game publisher Activision Blizzard used his image without his permission in Call of Duty: Black Ops II."
## [203] "Senator Angus King, the Maine independent who had backed another independent, Eliot Cutler, for governor, switched his allegiance to the Democrat, Representative Michael H. Michaud."
## [204] "Photos from Turkey, China, India and the USA."
## [205] "Thomas M. Menino, who served as Bostons mayor for 20 years, has died at the age of 71."
## [206] "Neil L. Rudenstine has presided over the library along with Anthony W. Marx, its president during planning for the institutions controversial renovation."
## [207] "Tim Cooks public announcement that he is gay has been met with support, but some point out that the battle for L.G.B.T. equality at work is far from over."
## [208] "David W. Dunlap has worked at NewYorkTimes for 39 years. He recalls the early news coverage of AIDS and compares it to NewYorkTimess coverage of the arrival of the Ebola virus in the USA."
## [209] "Democratic nostalgia seemed to be the theme of the night as former President Bill Clinton campaigned on Thursday for Gov. Andrew M. Cuomo."
## [210] "The French banks results were a turnaround from the previous quarter, when it took a charge of nearly 6 billion euros for legal penalties in the USA."
## [211] "Albert G. Horvath, the Smithsonian Institutions current senior finance official, will serve as secretary until David J. Skorton can take up his permanent role in July."
## [212] "James B. Stewart provides insight into his column about the Apple chief executives announcement that he is proud to be gay."
## [213] "The return of Janet R. Bender."
## [214] "On Tuesday, Michael S. Dell will try to persuade people that his company is about far more than the personal computers and computer servers it has been known for, with products intended for things as varied as the cloud computing networks of global..."
## [215] "The government has imposed billions of dollars in penalties on the big banks for wrongdoing. But, Peter J. Henning writes in the White Collar Watch column, that may not be enough to change banks behavior."
## [216] "C.H.C.M., the influential Bond Street shop, is debuting an in-house range of refined wardrobe essentials."
## [217] "Photos from Turkey, Syria, India and the USA."
## [218] "Relations between tech companies and the intelligence community has been strained recently, but Adm. Michael S. Rogers played down government concerns."
## [219] "Highlights from the International Herald Tribune archives: Europeans were happy Lyndon B. Johnson was elected in 1964."
## [220] "Millstein & Company, the financial advisory firm founded by James E. Millstein, has hired Mark Walker, a former banker with Rothschild."
## [221] "Despite efforts to improve disclosure under the Freedom of Information Act, the SEC continues to resist sharing data with the public, William D. Cohan writes in the Street Scene column."
## [222] "William A. Ackmans hedge fund could vote its stake in Allergan in support of ousting directors at a meeting scheduled for Dec. 18, bolstering his effort to force the company into a sale."
## [223] "Some post-shellacking advice for the POTUS."
## [224] "Photos from Israel, Algeria, Russia and the USA."
## [225] "John L. Thornton, the chairman of Barrick Gold and a professor at Tsinghua University in Beijing, was designated as a possible successor to Henry M. Paulson Jr. when they were at Goldman Sachs."
## [226] "A. Jerrold Perenchio, the former chairman of Univision, pledged to donate about 50 artworks including notable Impressionist paintings "
## [227] "Rita Doves poem November for Beginners, and the article Heavy Autumn Snowstorm Barrels Across Northeast, by Al Baker, Elizabeth A. Harris and Sarah Maslin Nir, appear in this pairing."
## [228] "An emboldened Speaker John A. Boehner warned President Obama against unilateral action to overhaul the immigration system."
## [229] "The 82 year old has been recording with her daughter, Patsy L. Russell, and John Carter Cash, for a project combining new songs and old folk and gospel tunes."
## [230] "The designation of MetLife as systemically important is a sign that regulators are looking ahead to the next possible crisis, Stephen J. Lubben writes in the In Debt column."
## [231] "The Democratic Senatorial Campaign Committee cancels $2 million worth of ads on behalf of Senator Mary L. Landrieu of Louisiana."
## [232] "Photos from Ukraine, Syria, Scotland and the USA."
## [233] "Barry C. Silk writes puzzles for grown-ups, but once in a while you can find stuff for less mature adults like me."
## [234] "The Sony Pictures Classics co-president will take over from Herbert S. Schlosser, who will be chairman emeritus."
## [235] "Vice President Joseph R. Biden Jr. spoke quickly on his relationship with Prime Minister Benjamin Netanyahu of Israel during remarks to the Jewish Federation."
## [236] "Mr. Morrison will play J.M. Barrie in the show about the creation of Peter Pan; he and Jeremy Jordan have both played the role in earlier productions."
## [237] "The term political correctness is perhaps most often used by its detractors but new research has found that thinking about being P.C. can actually improve creativity."
## [238] "Senator Patrick J. Leahy said he believes Loretta E. Lynch, President Obamas choice for attorney general, should be confirmed easily no matter when the Senate votes."
## [239] "By analyzing samples from each of the five NewYork City boroughs, Jane M. Carlton hopes to detect viruses before an outbreak."
## [240] "Is the food label \"natural\" misleading to consumers? Should the F.D.A. enforce a definition of the term?"
## [241] "Another small sampling of sparkling prose in recent editions, from serious to silly to Kenny G."
## [242] "President Obama and President Vladimir V. Putin of Russia discussed Iran, Syria and Ukraine during three brief conversations at the Asia-Pacific Economic Cooperation summit meeting."
## [243] "Andrea C. Bonomi, the Italian businessman battling a Chinese French bid for Club Mditerrane, announces a sweetened offer for the resort company."
## [244] "Reporters were surprised on Tuesday morning when the Federalist Society, a conservative legal group, announced that the news media would be barred from an appearance by Justice Samuel A. Alito Jr. at its annual gala black-tie dinner."
## [245] "Heaven, the first offering from the Birmingham raised, LosAngeles-based 24 year old T.O.L.D., premieres here."
## [246] "The activist investor William A. Ackman has acquired an 8.5 percent economic interest in the former animal health arm of Pfizer."
## [247] "Photos from France, China, Poland and the USA."
## [248] "Both steps fall short of the big breakup of Dow Chemical being advocated by the activist hedge fund manager Daniel S. Loeb."
## [249] "Mr. Grammer is to play the largely comic role of Charles Frohman, the theater producer of Peter Pan playwright J.M. Barrie."
## [250] "Photos from Turkey, HongKong, Afghanistan and the USA."
## [251] "Steven A. Ballmer, Microsofts former chief executive, is making a financial contribution that will enable the school to expand its computer science faculty by about 50 percent."
## [252] "Dassault Systmes, a French company, has a five year research agreement with the F.D.A. focused on using 3 D technology for simulating the reliability of pacemaker wires."
## [253] "Its in the bullet points: Shoes, bags, more mobile and social media, and a focus on the USA."
## [254] "Berkshire Hathaway, the conglomerate run by Warren E. Buffett, will acquire Duracell using a transaction aimed at lowering the tax bill."
## [255] "President Obama and Prime Minister Dmitri A. Medvedev of Russia at the East Asia summit plenary session in Naypyidaw, Myanmar, on Thursday."
## [256] "Readers wrote about a public reckoning for our wars in Iraq and Afghanistan and the sacrifices of our troops in comments on Daniel P. Bolgers Op-Ed, The Truth About the Wars."
## [257] "Warren E. Buffett has an appetite for large and creatively structured acquisitions. A look back at some of his recent deals."
## [258] "Advanced Placement courses can be a real boon to students motivated by intellectual curiosity and a love of learning. But for students looking to please their parents or for those in pursuit of transcript padding and other false academic idols, AP..."
## [259] "Thomas O. Kriegsmann is to be its director of programs, a new position that will include artist development projects."
## [260] "David W. Dunlap, a Metro reporter for NewYorkTimes, took us back to the moment when the paper stopped being produced with Linotype machines."
## [261] "Daniel S. Loeb, the activist hedge fund manager, has been pressing for a breakup of Dow Chemical and has been rebuffed in his attempt to name two directors."
## [262] "More than $40 million has been raised in honor of Joan H. Weill, who is stepping down as chairwoman of the Alvin Ailey Dance Foundation."
## [263] "Net neutrality could give Comcast a way out of its Time Warner Cable acquisition. | Warren E. Buffetts deal for Duracell satisfies a number of his deal-making preferences. | Virgin America to begin trading. | Goldman seeks to lure tech talent."
## [264] "Evan R. Chesler, the chairman of the law firm Cravath, Swaine & Moore, will take over as the library chairman from Neil L. Rudenstine."
## [265] "Finish Big and Succession, examine how entrepreneurs and corporate chieftains alike can be assessed on their departures, Jonathan A. Knee writes in a joint book review."
## [266] "Senator Mary L. Landrieus campaign has not hesitated to embrace her stance on the XL Pipeline ahead of her runoff election against Representative Bill Cassidy."
## [267] "Celebrities should not quit their day jobs, according to Andrew J. Ries."
## [268] "And what about that I.S.I. dreamboat Aasar Khan?"
## [269] "The alliance between the American and German companies is expected to strengthen Mercks oncology business in several important markets, including the USA."
## [270] "The animal health care company Zoetis, which has attracted the interest of the deal maker William A. Ackman, has made a deal of its own."
## [271] "The $66 billion acquisition by Actavis would be the largest deal this year and would thwart the takeover effort of Valeant and William A. Ackman."
## [272] "Representative Nancy Pelosi has a birthday wish for Speaker John A. Boehner."
## [273] "Insider trading cases are casting a spotlight on whether courts should defer to administrative agencies in determining criminal violations, Peter J. Henning writes in the White Collar Watch column."
## [274] "Representative Steve Israel joked about handing off the WashingtonDCC.C. chairmanship."
## [275] "Mayor Francis G. Slay said troops operating within the city of SaintLouis would have a secondary role in responding to any protests, and that police officers would be the ones dealing directly with demonstrators."
## [276] "What do you do when you are a billionaire and a movie buff? If you are Paul G. Allen, you turn the iconic theater you already own into a state-of-the-art venue."
## [277] "William A. Ackman and Valeant Pharmaceuticals lost their hostile battle to buy Allergan and still came away with a total of $2.6 billion."
## [278] "WallStreet is on pace for a big year of deal-making. | William A. Ackman may have lost Allergan, but he still came away a winner. | The Justice Department is weighing a civil suit against Angelo R. Mozilo, the former chief executive of Countrywide..."
## [279] "A briefing book for the White House chief of staff, Denis R. McDonough, had a note from one of his children attached to it as he sat in a meeting on Ebola in the White House on Tuesday."
## [280] "Much like their leader, Warren Buffett, the chief executives of Berkshire Hathaways subsidiaries are a savvy group of acquirers. Lawrence A. Cunningham, in the Another View column, explains why."
## [281] "American Ballet Theater and the Segerstrom Center for the Arts in Costa Mesa, Calif., will open the American Ballet Theater William J. Gillespie School on the Segerstrom campus in September, both organizations announced on Tuesday."
## [282] "Assembling the tale of G.M.s faulty ignition switch, which led to the deaths of at least 13 people, took months of digging by a team of reporters and editors."
## [283] "Joseph P. Clancy, acting director of the Secret Service, is to testify before Congress."
## [284] "Thomas B. Leonardi will be joining the investment bank as a senior adviser, focusing on the insurance industry."
## [285] "The release of an interview Robert Rubin gave nine years ago offers a window into the thoughts of the rich and powerful, William D. Cohan writes in the Street Scene column."
## [286] "On Saturday, the Arcangel-composed series Dances for the Electric Piano will be staged for the first time in the USA. Listen to it here."
## [287] "Representative Paul D. Ryan, the new chairman of the Ways and Means Committee, said that a broad overhaul of the tax code depended on the administrations engagement."
## [288] "Gov. Bobby Jindal of Louisiana tried to cut off an immigration discussion with Chuck Todd of NBC."
## [289] "The country plans to scrap its state monopoly on the sale of salt, a move that would mark the end of a system that can be traced back to 685 B.C."
## [290] "Despite being about to receive his Ph.D. in physics from Harvard, Kevin Niu plans to eschew the academic life to follow his calling in the entertainment industry."
## [291] "Daniel K. Tarullo, the Fed governor, described to a Senate subcommittee potential rules that could restrict banks from some types of commodities operations."
## [292] "Speaker John A. Boehner addressed President Obamas immigration actions."
## [293] "Is the Federal Reserve up to the task of regulating financial institutions that are so large and complex? the chairman of a Senate panel asked William C. Dudley."
## [294] "Attorney General Eric H. Holder Jr. offered guidance to law enforcement agencies and urged protesters to be restrained."
## [295] "Vice President Joseph R. Biden Jr. had to beat a hasty retreat on Friday at the sight of an unruly crowd of Ukrainians in Kiev protesting the new government amid continuing unrest in that former Soviet republic."
## [296] "Paul J. Taubman is bringing Don Cornwell, a managing director who specializes in sports team deals, to his boutique investment bank."
## [297] "At noon on Friday a handful of people performed a mock lynching across the street from the Old Courthouse in SaintLouis as a silent protest of the police killings of Michael Brown and Vonderrit D. Myers Jr."
## [298] "Last week was a rough one for the Federal Reserve. | Paul J. Taubman continues to poach talent from Morgan Stanley, his former employer. | Mathew Martoma has started his nine year prison term for insider trading."
## [299] "The Chapter 11 filing by Aereo prompts a larger question of whether bankruptcy courts should actively police debtors motives for filing cases, writes Stephen J. Lubben in the In Debt column."
## [300] "The Fed has to balance the conflicting roles it plays as an overseer of the banks and as a law enforcement agency pursuing misconduct, Peter J. Henning writes in the White Collar Watch column."
## [301] "Speaker John A. Boehner announced that he was reappointing Representative Trey Gowdy, Republican of SouthCarolina, as chairman of the special committee on Benghazi for the 114th Congress that begins in January."
## [302] "Senator Elizabeth Warrens wrath toward Antonio F. Weiss is misdirected, and her understanding of the inversion deal on which she bases her opposition appears misinformed."
## [303] "Senator Charles E. Schumer said Tuesday that it was a political mistake to pass the Affordable Care Act in 2010 because voters at the time were looking for relief from the recession not universal health care."
## [304] "Photos from Egypt, HongKong, Kenya and the USA."
## [305] "The return of Michael S. Maurer."
## [306] "Thanksgiving was my Korean familys annual recommitment ceremony to the USA."
## [307] "The potential political costs of the civil unrest in Ferguson, Mo.; speculation about Chuck Hagels replacement; and a look at Speaker John A. Boehners turkey brine recipe."
## [308] "StevenACohens ex-wife gets outside financing for her lawsuit. | Poison puts develop a downside. | Trustee moves to repay Mt. Gox creditors. | Philip A. Falcone to Depart the Harbinger Group."
## [309] "Photos from HongKong, China, Syria and the USA."
## [310] "In Newport, R.I., visitors can tour storied mansions decorated for Christmas."
## [311] "Michael B. Jordan and John Turturro will take part in a reading of the script for Spike Lees Do the Right Thing"
## [312] "David W. Dunlap, a Metro reporter and the writer of the Building Blocks column, explored the sometimes close connection between Mr. Castro and NewYorkTimes."
## [313] "A wide open grid from Elizabeth C. Gorski."
## [314] "Celebrate the holidays with Elizabeth C. Gorski."
## [315] "Programs, and payouts, to encourage whistle-blowers to come forward with wrongdoing are likely to increase with the new Congress, Peter J. Henning writes."
## [316] "Jeh C. Johnson, the Homeland Security secretary, is prepared to defend the presidents decision to take executive action to overhaul the immigration system."
## [317] "Photos from HongKong, Paris, SouthKorea and the USA."
## [318] "The reporters J. David Goodman and Michael Wilson described a suburban mothers descent to a heroin addict whose home on Staten Island served as a drug-dealing center."
## [319] "Chief Justice John G. Roberts Jr. is well known for his ability to quote obscure case law, but on Monday he invoked the rapper Eminem at the Supreme Court."
## [320] "Mary L. Landrieu, a three term senator, is in the race of her career against the Republican Bill Cassidy as Louisiana voters head to the polls for a runoff vote on Dec. 6."
## [321] "Bank of NewYork Mellon said on Tuesday that it would give a board seat to Edward P. Garden of the hedge fund Trian Fund Management, forestalling a potentially costly proxy fight."
## [322] "The deal for IndCor Properties will make Singapores sovereign wealth fund a major owner of warehouses and distribution centers in the USA."
## [323] "Speaker John A. Boehner on options that Republicans are considering to block President Obamas immigration action."
## [324] "Senator Joseph R. McCarthy was censured 60 years ago today."
## [325] "Jonah M. Kessel filmed a documentary video in China and Myanmar, where jade mines have become a free market for heroin, and tells of the story of an addict he encountered."
## [326] "In a spellbinding letter to Jim Cramer, the activist investor J. Carlo Cannell urged him to cut his pay 70 percent, resign from CNBC and direct his energy to helping your fellow shareholders crawl back from Hades."
## [327] "The Fox affiliate in NewOrleans is taking issue with the use of its news anchors words in a campaign ad for Senator Mary L. Landrieu."
## [328] "NewYorks chokehold case has thrown a volatile element into Loretta E. Lynchs confirmation hearings as USA attorney general."
## [329] "Senator Elizabeth Warrens opposition to a nominee for a role at the Treasury Department demonstrates her blatant political motivation, William D. Cohan writes in the Street Scene column."
## [330] "J. Carlo Cannell wants Jim Cramer to sell TheStreet or take a pay cut and quit CNBC. A sale may be wishful thinking, says Quentin Webb of Reuters Breakingviews. But Mr. Cramers payout is hard to justify."
## [331] "The appellate division of the NewYork State Supreme Court dismissed a lawsuit at the heart of a bitter dispute between the billionaire collector Ronald O. Perelman and the art dealer Larry Gagosian."
## [332] "Photos from Russia, SouthAfrica, India and the USA."
## [333] "Andrea C. Bonomi, the Italian businessman fighting a Chinese investor for Club Mditerrane, raised his bid to $29.65 a share for the French resort operator."
## [334] "President Obama on Friday announced his selection of Ashton B. Carter to lead the Pentagon, White House officials said, embracing a physicist and national security centrist who may advocate a stronger use of American power."
## [335] "Jon Caramanica and A.O. Scott discuss what this update of The Bodyguard gets right about the music business."
## [336] "The famously outspoken, leather-clad architect is being honored this week with Design Miamis inaugural Design Visionary Award and an exhibition at the Bass Museum of Art. In this video, he discusses his career with T."
## [337] "An architect who hopes to save a Paul Rudolph-designed building in Orange County, N.Y., presented his detailed proposal to county leaders there on Friday."
## [338] "Demonstrations focused on fatal police encounters continued on Saturday, with a rally at the Louis H. Pink Houses in Brooklyn to protest the fatal shooting of Akai Gurley there and a grand jurys decision not to indict an officer in the death of..."
## [339] "Will the SECs treatment of Bank of America, Peter J. Henning asks in the White Collar Watch column, become the norm for other banks seeking waivers from rules on bad actors?"
## [340] "After years of research, there is genuine hope for reducing the toll exacted by lung cancer, which is the leading cause of cancer deaths in the USA."
## [341] "Senator Charles E. Schumer of NewYork may be Republicans favorite Democrat these days."
## [342] "Recommendations from the American Bankruptcy Institute failed to address derivatives held by nonfinancial institutions that will keep operating, writes Stephen J. Lubben in the In Debt column."
## [343] "Readers expressed outrage and despair in comments on a Friday editorial, It Wasnt Just the Chokehold: Eric Garner, Daniel Pantaleo and Lethal Police Tactics, and on an Op-Ed from Eric L. Adams, We Must Stop Abuse of Black Men."
## [344] "Jessica Bibliowicz will become chairwoman of the Cornell Weill Medical College, effective Jan. 1. Her father, Sanford I. Weill, has been chairman for two decades."
## [345] "The SecondMarket exchange won all but one of the 20 Bitcoin blocks in the governments auction. The venture capitalist Timothy C. Draper, who swept the first auction, won only 2,000 Bitcoins this time."
## [346] "Brian T. Moynihan tells a conference that his bank is expecting trading revenue in the fourth quarter to be down from last quarter and from a year earlier."
## [347] "The revival of A.R. Gurneys play will close this Sunday, before some of its scheduled stars ever get to perform."
## [348] "Jonathan Gruber, the M.IT economist who advised the Obama administration on the Affordable Care Act, apologized on Tuesday for inflammatory comments that have brought negative attention to the law in recent months."
## [349] "The Senate Intelligence Committee has released its long-awaited review on the torture of prisoners held by the Central Intelligence Agency during the George W. Bush administration."
## [350] "Just hours after announcing its $8.4 billion acquisition of Cubist Pharmaceuticals, a Delaware judge invalidated patents owned by the antibiotics maker. Merck appears to have been caught up in the M.&.A. exuberance, says Robert Cyran of Reuters..."
## [351] "Recent criticism of James J. Cramers compensation from TheStreet.com has painted him as another greedy WallStreet executive looking out for himself but not shareholders."
## [352] "Gov. Chris Christie of NewJersey, who was appointed USA attorney for the state by President George W. Bush in the weeks after the Sept. 11 attacks, seemed not eager on Wednesday to discuss the Senate report on the brutal American..."
## [353] "In this lesson we offer three different teaching ideas to engage students in the debate over immigration policy in the USA."
## [354] "The latest appeals court decision will make some insider-trading cases harder, but it does not give a free pass to hedge funds to trade on any confidential information, Peter J. Henning writes in White Collar Watch."
## [355] "The latest NewYorkTimes poll is based on telephone interviews conducted Dec. 4 through 7 with 1,006 adults throughout the USA."
## [356] "The Confucius Peace Prize was first given out in late 2010 as a rejoinder to the Nobel Peace Prize. President Vladimir V. Putin of Russia is a past recipient."
## [357] "Were going to continue to press his nomination forward, Treasury Secretary Jacob J. Lew said. He also spoke about corporate taxes and sanctions on Russia."
## [358] "George R. Goldner will become a private art adviser after 21 years heading the department. Nadine M. Orensteinhas been named to succeed him."
## [359] "Lloyd C. Blankfein, Goldman Sachss chief executive, addressed the antagonism that has been coming out of Washington."
## [360] "Francis J. Shammo, Verizons chief financial officer, said the company planned to continue to invest in its FiOS fiber-optic network and its wireless systems regardless of the outcome of the broadband debate."
## [361] "Senator Dianne Feinstein, chairwoman of the committee that investigated the CIAs interrogation program, offered a live rebuttal as John O. Brennan, the agencys director, responded to the committees findings."
## [362] "President Obama and Vice President Joseph R. Biden Jr. both made calls to Democratic lawmakers on Thursday in an urgent effort to save a $1.1 trillion spending bill that the White House and Republicans say is a worthy compromise."
## [363] "The Blackstone Group, the private equity giant run by Stephen A. Schwarzman, is almost finished raising a new fund for energy investments that is expected to exceed $4 billion in assets."
## [364] "OscarSSchafer will succeed the current NewYork Philharmonic chairman, Gary W. Parr, early next year."
## [365] "Friday and the weekend: a struggle for L.G.B.T. protections, more chilly weather, protests on Saturday, and the week in pictures."
## [366] "An A.C.L.U. video features military personnel who objected to the Bush-era torture program."
## [367] "Two new books provide insight into lesser-known stages of Charles M. Schulz and Jack Kirbys careers."
## [368] "On this day in 2000, the Supreme Court ruled 5 to 4 that there would be no additional counting of presidential votes in Florida. 35 days after Election Day, the decision effectively handed the presidency to George W. Bush."
## [369] "Hours after casting his final vote as a member of Congress, Representative John D. Dingell was hospitalized after taking a spill earlier in the week."
## [370] "Jungle Lady is the first single off the NewYork-based act Lion Babes self-titled debut EP."
## [371] "The Securities and Exchange Commission said it would not be able to proceed with the lawsuit because the two main witnesses it intended to call to testify are in Poland and had no plans to return to the USA."
## [372] "The return of Joel D. Lafargue, after a long break."
## [373] "Nominees that didnt make the final cut this year include Nine Inch Nails, Sting, N.W.A, Chic and Kraftwerk."
## [374] "The disturbing statistics on military rape and the fleeting attention of the public led Mary F. Calvert to document the stories of those who survived assaults."
## [375] "James E. Staley, a managing partner at the NewYork hedge fund BlueMountain Capital Management, is expected to be considered for the board during UBSs annual meeting in May, and to join the banks risk committee."
## [376] "Wednesday: The M.T.A. honors its workers, oddly warm weather, and a vigil for Pakistan."
## [377] "President Obama is scheduled to speak about relations with Cuba at 12 p.m. ET."
## [378] "Highlights and video of the remarks by President Raul Castro of Cuba on a diplomatic breakthrough with the USA."
## [379] "Speaker John A. Boehner on Wednesday called President Obamas new policy on Cuba the latest in a long line of mindless concessions to a dictatorship that brutalizes its people and schemed with our enemies."
## [380] "Alan P. Gross prepared his remarks at his attorneys office in Washington on Wednesday."
## [381] "Most Americans support normalizing trade and diplomatic relations with Cuba and consider what happens in the communist nation to be important to the interests of the USA."
## [382] "Photos from Pakistan, Australia, France and the USA."
## [383] "President Obama took the occasion of Alan P. Grosss release not only to acknowledge the interest the Jewish community had taken in Mr. Grosss case, but also to connect his release to the holiday and to Jewish teaching."
## [384] "Curtis L. Buser, who has been interim finance chief for the last seven months, is taking over the job permanently."
## [385] "Speaker John A. Boehner posted a holiday greeting on Thursday, a video poem title Happy Christmas to All, although the Speakers wishes may not extend all the way to the White House."
## [386] "Recommended books for learning more about Cubas history and its relationship to the USA."
## [387] "In a speech on Thursday, Benjamin M. Lawsky, NewYork States top financial regulator, provided the most detail yet on revisions to his proposed Bitcoin rules."
## [388] "The firm promoted John E. Waldron, one of its star deal makers, to become a co-head of its investment bank, succeeding a scion of one of Goldmans most famous families."
## [389] "The disposal is the latest move for Xerox, which has been transformed under the leadership of Ursula M. Burns into a provider of various business services."
## [390] "Speaker John A. Boehner has invited President Obama to address the country on Jan. 20 and give a State of the Union speech before a joint session of the new Republican-controlled Congress."
## [391] "Mark A. Flaherty and Mark O. Winkelman will serve on a board that now has 14 members."
## [392] "Goldman names John E. Waldron co-head of its investment bank. | Regulators deem MetLife too big to fail. | Another big whistle-blower reward in Bank of America case. | London tenants win battle over USA equity firm."
## [393] "After an investigation into conflicts of interest, William C. Erbey has agreed to step down from his position as chairman of Ocwen and four other related companies."
## [394] "Its about non-digital IT."
## [395] "Fred R. Conrad may be best known for his exquisite portraits, but an assignment in Kosovo taught him the value of watching and waiting for the story to come to him."
## [396] "An overhaul of the Freedom of Information Act did not survive the legislative deal-making that produced the spending plan. William D. Cohan laments that loss in the Street Scene column."
## [397] "Its hard to be FDR."
## [398] "Ocwen should look at Clayton Homes, whose credo puts customers first, writes Lawrence A. Cunningham in Another View."
## [399] "Photos from Ukraine, India, Nepal and the USA."
## [400] "NewYorkTimes reporter David W. Dunlap explains how coverage of the Bay of Pigs disaster unfolded."
## [401] "After decades of oppression of people who identify as L.G.B.T., the Cuban government now pays for gender reassignment surgery."
## [402] "Look for the hidden message from Peter A. Collins."
## [403] "The new year will bring cases from the subprime mortgage crisis, a Justice Department decision on an appeal of an insider trading ruling and, perhaps, new accounting frauds, Peter J. Henning writes in the White Collar Watch column."
## [404] "Under the F.D.A.s new policy, any man who has had sex with another man in the last year may not donate."
## [405] "Representative Michael G. Grimm of NewYork insisted last week that he would not resign from his seat. On Monday night, he changed his mind."
## [406] "Speaker John A. Boehner gave his seal of approval to Representative Michael Grimms resignation."
## [407] "David E. Sanger, chief Washington correspondent for NewYorkTimes, recalled a visit to the country 25 years ago."
## [408] "Former President George H. W. Bush was released from a Houston hospital where he has been staying since last week after experiencing shortness of breath."
## [409] "Speaker John A. Boehner expressed his support for Representative Steve Scalise, who spoke at a conference for white supremacists in 2002."
## [1] "Remaining Acronyms in Abstract:"
## [1] "In the 1864 election, Gotham delivered nearly twice as many votes to the presidents opponent, George B. McClellan."
## [2] "Researchers are finding more evidence that women who take S.S.R.I. depressants like Prozac and Zoloft increase the likelihood of a variety of health problems in their newborns."
## [3] "Eric T. Schneiderman, NewYorks attorney general, filed a suit on Tuesday that accuses Evans Bank of denying mortgages to African-Americans in Buffalo regardless of their credit."
## [4] "Jeffrey H. Knox, a senior federal prosecutor who butted heads with a number of WallStreet banks, is switching sides."
## [5] "In which Peter A. Collins disproves Aesop."
## [6] "Antonia M. Apps, the lead federal prosecutor in the criminal trial last year of Michael Steinberg, is taking a job with the law firm Milbank, Tweed, Hadley & McCloy."
## [7] "Scholarly musicians prove that Ph.D.s can play."
## [8] "In this new series, Chris Labzda and Bon Duke, the co-founders of the NewYork Fashion Film Festival, curate a short film each week for T. This weeks installment: A collage of classics overlaid with sound from Lauren Wolksteins Social Butterfly."
## [9] "I moved to Hudson, N.Y., to slow down and save money. I didnt know Id feel so lonely."
## [10] "A study suggests that exercise can help kids, especially those with A.D.H.D., focus in class."
## [11] "How should the USA respond to the killings of Steven J. Sotloff and James Foley?"
## [12] "Although banks are hiring armies of legal and compliance professionals in response to settlements and fines, they would do better to find ways to streamline their existing legal work, Geoffrey A. Moore and Mark Harris write in an Another View column."
## [13] "William T. Shermans 1864 campaign to take the Georgia city was one of the bloodiest of the Civil War."
## [14] "George Martin, author of the Song of Ice and Fire series, will be promoting his new book at the 92nd St. Y."
## [15] "The workers are OK."
## [16] "The Securities and Exchange Commission has chosen Tracey L. McNeil, an agency veteran and former corporate lawyer, to act as a liaison in resolving problems that retail investors may have with the agency."
## [17] "John A. Paulsons hedge fund suffered across-the-board losses in July and did only marginally better in August, according to an update to investors."
## [18] "Mr. Sheen will direct and perform in Dylan Thomass work he called it a play for voices on Oct. 26 at the 92nd Street Y."
## [19] "The acquisition is the latest move by Jeffrey R. Immelt, the chief of GE, to refocus the conglomerate on its core industrial businesses."
## [20] "The Chinese state news media has cited concerns that the USA is indoctrinating Chinese students by including its founding documents upholding freedom and human rights in the SAT."
## [21] "At the midpoint of the range, the Citizens Financial Group, based in Providence, R.I., would be valued at $13.4 billion."
## [22] "Mark P. Frissoras decision comes a few weeks after Carl C. Icahn disclosed an 8.48 percent stake in the rental car provider."
## [23] "Martin Lipton is virtually guaranteeing the continuation of policies opposed by many NYU faculty members, William D. Cohan writes in the Street Scene column."
## [24] "China Central Television used images of Condoleezza Rice in a report about a Beijing visit by Susan E. Rice, President Obamas national security adviser."
## [25] "If youve never heard the name Lloyd J. Austin, theres a good reason"
## [26] "This weeks video features Ismenia Mendes and David McElwee in a scene from A. R. Gurneys 1977 drama The Wayside Motor Inn."
## [27] "The media and financial data company Thomson Reuters is looking to sell peHUB, Buyouts and Venture Capital Journal, according to a report by peHUB."
## [28] "News headlines briefly flashed that Carl C. Icahn had raised his stake in Gannett, the media company, to nearly 9 percent. But as it turns out, his firm simply made a typo in a regulatory filing."
## [29] "Just two days after her USA Open victory, Serena Williams hosted her first ever fashion show for HSN."
## [30] "Nearly 70 years after they parted, a Chinese veteran is on a quest to find a Japanese woman he met shortly after World War II. His tale offers a warmer recollection of that era."
## [31] "In this lesson we offer a series of topics and questions paired with Times essays, articles, slide shows and videos to help students dig deeper into the causes, effects and overall legacy of World War I."
## [32] "A collection of material related to Rosa Parks, bought last month by Howard G. Buffett, is being lent to the Library of Congress for 10 years."
## [33] "A soldier takes time out of uniform to pursue an M.F.A. in writing, and discovers that what sets him apart, as a veteran at the keyboard, is not as important as the common ground he shares with other writers."
## [34] "Worn by the chef from July 29 to Aug. 4 while cooking at his two restaurants, Blue Hill in Manhattan and Blue Hill at Stone Barns in Pocantico Hills, N.Y."
## [35] "Gov. Andrew M. Cuomo and Mayor Bill de Blasio both enjoyed an Italian sausage at the Feast of SanGennaro in Little Italy on Saturday."
## [36] "Eike Batista was accused of manipulating the share price of his now-bankrupt petroleum company OGX."
## [37] "The activist investor William A. Ackmans new fund, Pershing Square Holdings, aims to list on the the Euronext Amsterdam exchange on Oct. 13."
## [38] "Fraud in the market for penny stocks continues unabated, two criminal cases filed last week show, Peter J. Henning writes in the White Collar Watch column."
## [39] "Quarries and connecting trenches were nothing less than small cities, where the walls bear witness to the people who fought and died in World War I."
## [40] "The deal for Vkontakte ends tensions between the social networks minority shareholders and Alisher B. Usmanov, the Russian billionaire who controls Mail.ru."
## [41] "Sears is borrowing $400 million from its chief, the billionaire Edward S. Lampert, through his hedge fund."
## [42] "KKRs purchase of Pioneers D.J. audio equipment business is the modern-day equivalent of providing gold-rush pickaxes to participants in the rave rush, Jeffrey Goldfarb writes in Reuters Breakingviews."
## [43] "Two grandchildren of Dwight D. Eisenhower say proposed changes by Frank Gehry to his memorial to the former president do not satisfy their concerns."
## [44] "In a letter to Electra Private Equity shareholders, the activist investor Edward J. Bramson said a change in approach in Electras strategy could increase the value of its shares by about $1.6 billion."
## [45] "Prosecutors in Brazil have argued that Mr. Batista profited from insider information when he sold shares in his now bankrupt petroleum company, OGX."
## [46] "Comments by Marshall L. Miller, the No. 2 official in the Justice Departments criminal division, reflect the agencys renewed interest in charging individual bank employees rather than just the banks."
## [47] "The top job at the database giant will be shared by Mark V. Hurd, now co-president, and Safra Catz, who is co-president and chief financial officer."
## [48] "Alibaba is set to start trading on the NewYork Stock Exchange under the ticker symbol BABA. | Markets showed relief that the United Kingdom would not face a tumultuous breakup. | Lawrence J. Ellison announced his retirement as chief executive of Oracle."
## [49] "Average waist circumference but not B.M.I. has increased significantly in the USA, a new study reports."
## [50] "The British private equity firm accused Edward J. Bramsons Sherborne Investors Management of making unverifiable statements and unsubstantiated claims in its effort to reshape Electra."
## [51] "Alibaba soared in its public market debut. | Siemens agreed to buy the Dresser-Rand Group. | EMC weighed a deal with H.P. | Public pension funds may be souring on hedge funds."
## [52] "Venezuela may represent the Latin American and Caribbean nations and sit right next to the USA."
## [53] "Speaker John A. Boehner seemed to blame lazy Americans for the stalled economy."
## [54] "It was unclear whether President Petro O. Poroshenko would travel to NewYork to deliver his address as scheduled on Thursday."
## [55] "As the reclusive genius Richard D. James releases his first album in 13 years, the graphic designer with whom he has frequently collaborated discusses their work together."
## [56] "Vice President Joseph R. Biden Jr. met Carina Castro, the daughter of Julin Castro, secretary of Housing and Urban Development, at a reception honoring Hispanic Heritage Month."
## [57] "Attorney General Eric H. Holder Jr. will announce Tuesday that the federal government will end its fiscal year next week with 4,800 fewer prisoners, the first time since 1980 that the inmate population has declined from year to year."
## [58] "President Obama could end up leaving a smaller imprint on the judiciary than either President Bill Clinton or President George W. Bush."
## [59] "Ever wonder why jewelry stores hide the price tags? How invoice factoring works for small businesses. And why German companies are on a buying spree in the USA."
## [60] "Speaker John A. Boehner urges the new Veterans Affairs secretary to look to the private sector in reviewing how the department delivers health care to veterans."
## [61] "On this day in 1952, Richard M. Nixon gave his famous Checkers speech."
## [62] "To kick off Paris Fashion Week, the chic native whose shirting-based collection is stocked at Barneys NewYork and Dover Street Market shares a list of her favorite places around town with T."
## [63] "Gov. Paul R. LePage of Maine wants to help the NFL address its domestic violence problem."
## [64] "Maurice R. Greenberg, the former chief of AIG, has now raised several million dollars from three WallStreet investors to help cover the cost of the case."
## [65] "Electra said that two influential shareholder advisory services opposed efforts of the activist investor Edward J. Bramson, through his Sherborne Investors Management, to shake up the board of the British private equity firm."
## [66] "Representative James A. Traficant was seriously injured after a tractor accident on his family farm Tuesday night, according to local media reports in Youngstown, Ohio."
## [67] "The role of former President Gerald R. Ford in the filing of a friend-of-the-court brief in a Michigan affirmative action case."
## [68] "The insider trading case against Michael A. Lucarelli, a former executive at Lippert/Heilshorn and Associates, was relatively minor. But he got attention for running barefoot from the court in August."
## [69] "Judge Richard M. Berman of Federal District Court in Manhattan, before sentencing conservative author Dinesh DSouza to probation for violating federal campaign finance laws."
## [70] "Speaker John A. Boehner, an avid golfer, is gearing up for the Ryder Cup, the intense competition between the USA and Europe that begins Friday in Scotland."
## [71] "Speaker John A. Boehner tells First Draft that the new Congress should debate military action in Syria."
## [72] "A new poll has Senator Mark R. Warner, the Democratic incumbent in Virginia, up by 9 points over his Republican opponent."
## [73] "Eric H. Holder Jr.s resignation as attorney general means that President Obama is losing one the longest-serving members of his cabinet and one of his closest confidants."
## [74] "The Department of Labor is putting money behind its push to expand paid family leave in the USA. The issue could be a factor in coming elections if enough voters ask about it."
## [75] "Members of the NewYork Police Department turned out in force on Thursday for the funeral of Officer Michael Williams in LaGrangeville, N.Y."
## [76] "How big a fight will President Obama put up over Attorney General Eric H. Holder Jr.s successor? The question is captivating Washington."
## [77] "Eric H. Holder Jr. has resigned as attorney general, but hes in no hurry to go anywhere."
## [78] "William H. Gross, who built Pimco into one of the largest asset managers in the world, will join Janus Capital after a decision had been made for him to leave Pimco or be forced out, said a person briefed on the matter."
## [79] "William H. Gross, for decades an investment guru, was undone by the increasing complexity of managing his ever-growing fund."
## [80] "Two of Allergans largest shareholders, T. Rowe Price and Pentwater Capital Management, have broken their silence to insist that the company not strike any deals before a scheduled special meeting in December."
## [81] "As Yahoo tries to figure out what to do after raising $6 billion from its stake in the Alibaba Group, a prominent investor, Starboard Value, has emerged to offer some suggestions including buying AOL."
## [82] "Speaker John A. Boehner says Republicans must compete in Northeast"
## [83] "The ruling by Judge Thomas P. Griesa of the Federal District Court in Manhattan allows Citigroup to make a $5 million payment to bondholders."
## [84] "Remembering James A. Trafficant, in videos and soundbites."
## [85] "The low-power computer server that has been seen as a way to revive Hewlett-Packard has never lived up to expectations. Still, HP keeps trying, this time with a chip made by ARM."
## [86] "William H. Gross abruptly leaves Pimco. | AIG case sheds new light on bailout. | DreamWorks Animation in sale talks with SoftBank. | Allergan shareholders speak out."
## [87] "Judge Thomas P. Griesa of Federal District Court in Manhattan stopped short of issuing sanctions, saying he would make a decision about them in the future."
## [88] "A suit filed by Stephen A. Wynn, the casino impresario, against an investor he accuses of slander is the latest salvo in the growing tensions between vocal shareholders and corporations."
## [89] "Kyle T. Dolan spins us a puzzle."
## [90] "The move, which the activist investor Carl C. Icahn had called for, will cleave eBay almost in half and separate it from a company that generates almost half its revenue."
## [91] "The team behind the popular Lower East Side restaurant are publishing a collection of 100 seasonal dishes meant to provide both information and inspiration. Here, they share a recipe with T."
## [92] "Elizabeth C. Gorski gives us the lowdown."
## [93] "The British media and marketing firms acquisition of Advanstar, a private company, would create the largest events organizer in the USA."
## [94] "The initial public offering of Pershing Square Holdings is expected to give the activist investor William A. Ackman a permanent pool of capital to make bigger, bolder bets."
## [95] "James R. Clapper Jr., the director of national intelligence, sent a message to employees on Tuesday defending the nations spy agencies against criticism."
## [96] "David A. ONeil has not announced his next step, though he will probably swing through Washingtons revolving door."
## [97] "A Hungarian musicologist finds Mozarts own score of the Piano Sonata in A, K. 331."
## [98] "After 20 years on the board of the Alvin Ailey Dance Foundation, Joan H. Weill, its chairwoman, is expected to announce Thursday that she is stepping down at the end of the year."
## [99] "Several recent bankruptcy cases and news events have shown that municipal and corporate debt securities have faced issues in pricing and trading, Stephen J. Lubben writes in an In Debt column."
## [100] "The countrys largest banks are rolling out a new service that could help them lessen their reliance on expensive data terminals like those sold by Bloomberg L.P."
## [101] "What White House security breaches happened during the presidencies of Franklin D. Roosevelt, Herbert Hoover and Ronald Reagan?"
## [102] "Politicians and NewYorks elite gathered Wednesday for the Alfred E. Smith charity dinner and roast."
## [103] "George W. Bush talks about his brother Jebs presidential aspirations."
## [104] "Michael R. Bloomberg and two Disney princesses."
## [105] "Former President George W. Bush said on Thursday that he had been lobbying his younger brother Jeb to throw his hat in the ring in 2016"
## [106] "The government said the criminal charges against Michael J. Coscia, founder of Panther Energy Trading, were the first to be brought under new rules that bar a type of abusive trading called spoofing."
## [107] "The Solomon R. Guggenheim Museum is planning the construction of a new building somewhere in NewYork City that will be used for offices, art storage and some public programming."
## [108] "Speaker John A. Boehner has picked Elise Stefanik, who is running for an open House seat in upstate NewYork, to deliver the partys radio address on Saturday."
## [109] "As the billionaire Kenneth C. Griffin battles his wife in divorce court, he has drawn back the curtain on one of the most prominent marriages in hedge fund world by disclosing terms of their prenuptial agreement."
## [110] "Jesse C. Litvak had been one of the few people convicted of fraud over the bailout of WallStreet, but an appeals court suggested that his conviction was likely to be overturned."
## [111] "Before my son started school, I shared my parents hippie view of the P.T.A. as a perfect-parent filled hassle and just another part of The Establishment trying to squelch creativity out of learning."
## [112] "Vice President Joseph R. Biden Jr. held a question and answer session at Harvard on Thursday."
## [113] "The investor Edward J. Bramson had been pushing to shake up the board of the British private equity firm Electra Private Equity and had sought two seats on its board."
## [114] "Including hard terms for AIG was politically necessary to getting the TARP program going, but Henry M. Paulson Jr. said he did support the bailout package and its terms."
## [115] "These D.I.Y. magazines are on the rise in LosAngeles, where artists are using them as a showcase for their work."
## [116] "A growing body of research indicates that many people who react to gluten may be suffering a condition called non-celiac gluten sensitivity, or NCGS."
## [117] "A big hurdle in the spoofing case against a high-frequency trading firm is that a jury must decide whether one computer fooling another is a crime, Peter J. Henning writes in the White Collar Watch column."
## [118] "Just 24 percent of respondents to a Pew Research Center poll correctly picked Janet L. Yellen as the Feds chairwoman from a list of four names."
## [119] "Maurice R. Greenberg, AIGs former chief and a large shareholder, has spun a ludicrous tale in court that the bailout of the insurer was unfair to its investors."
## [120] "Duncan L. Niederauer, who recently retired as head of the NewYork Stock Exchange, has joined an upstart brokerage firm called Battery East, which aims to help employees of privately held companies sell their shares."
## [121] "Big banks face another round of USA charges. | Henry M. Paulson Jr. testifies that punitive AIG terms were necessary. | Investors cheer the breakup of HP. | Lawyers for Goldman and Libyas sovereign fund clash in court."
## [122] "Timothy F. Geithner, the former Treasury secretary, was a witness in the trial of a lawsuit over the departments role in the bailout of AIG His book was under scrutiny, too."
## [123] "By chronicling the building blocks of technological advances, Walter Isaacsons The Innovators hopes to teach us about the nature of innovation, Jonathan A. Knee writes in a review, the debut Book Entry column."
## [124] "President Obama continues to get low marks on his handling of the threat from the Islamic State, also known as ISIS or ISIL."
## [125] "Valeant and Pershing Square are planning to raise their offer for Allergan. | Profit at Goldman less easy to find. | AIG trial puts Timothy F. Geithner on the hot seat. | Glencores chief pursues Rio Tinto."
## [126] "A derivatives rule is set to change. | JPMorgans data breach causes alarm. | Chinese companies are scooping up real estate across the globe. | Former Treasury Secretary Timothy F. Geithner defends the bailout of AIG"
## [127] "The $2.6 billion deal, which includes debt, is the latest transaction to reshape the drug industry. Auxilium is terminating its deal to acquire QLT."
## [128] "Carl C. Icahn, the billionaire activist investor, sent a letter to Timothy Cook, the chief executive of Apple, saying that the company is hugely undervalued."
## [129] "Critics want a war memorial to remove a sculpture based on Alfred Eisenstaedts famous Life Magazine photo of a sailor kissing a nurse at the end of World War II."
## [130] "One way to level the playing field is to simply repeal the safe harbors in the bankruptcy code, writes Stephen J. Lubben in the In Debt column."
## [131] "Facebook has a new local mobile advertising play. J.C. Penney is in the midst of an e-commerce renaissance."
## [132] "The AM1 Supreme by MARCH LA.B bears the hallmarks of vintage watches, but with a streamlined aesthetic."
## [133] "The government acknowledged there was scant legal precedent for its demand for $1.6 million in restitution from Jesse C. Litvak, who was convicted of securities fraud in March."
## [134] "Ben S. Bernanke, the former Federal Reserve chairman, kept his answers brief on the stand in the lawsuit over the 2008 bailout of American International Group."
## [135] "A sluggish global outlook sends ripples through the markets. | Tuning up UBSs investment bank. | Symantec announces a split. | Ben S. Bernanke defends the AIG bailout."
## [136] "The business will merge with a fledgling firm founded by Paul J. Taubman, the former Morgan Stanley investment banker, who will run the combined firm as chief executive and chairman."
## [137] "If you want to time-travel to the 1990s, youll get a chance at 1 p.m. today, when the William J. Clinton Library releases 10,000 more pages of previously undisclosed documents from the Clinton years."
## [138] "Field + Supply, founded by the interior designer Brad Ford, launches this weekend in a barn in High Falls, N.Y. Here, a look at some of the furniture and objects on offer."
## [139] "Ben Ratliff and Jon Caramanica discuss the singers Tinashe and FKA twigs, and whether they point toward a new conception of R&B."
## [140] "The Army War College rescinded the masters degree of Senator John E. Walsh on Friday, determining that the Montana Democrat plagiarized his final paper there in 2007."
## [141] "A restaurant learns what happens when you tell customers to pay what God wants them to pay, Microsofts chief executive says its O.K. for women to ask for a raise, and new restaurants are making Detroit a culinary oasis."
## [142] "A new USAO. lounge for service members and their families has opened at Terminal 5 at Kennedy Airport in NewYork."
## [143] "Vice President Spiro T. Agnew resigned on this day in 1973."
## [144] "Holly Fallon, whose stage name is Dollicious, and Miriam Hintz, who goes by Haru, posed in the lower courtyard of the Jacob K. Javits Convention Center on Friday at NewYork Comic Con."
## [145] "Two cases raise the question about what role the courts should play in policing negotiations and the limits that can be applied to the tactics one side can use in making a deal, Peter J. Henning writes in the White Collar Watch column."
## [146] "The awards will be announced on Nov. 23 in a show broadcast by ABC."
## [147] "A 28 year old digital communications director is helping to turn Speaker John A. Boehner, no ones idea of a digital-age guru, into a YouTube star."
## [148] "Abigail Johnson, 52, who is president of the parent company of Fidelity Investments, will succeed her father, Edward C. Johnson III, as chief executive."
## [149] "Photos from HongKong, Syria, Yemen and the USA."
## [150] "Adam G. Perl puns his way through our Tuesday puzzle."
## [151] "JPMorgan Chase posts a third quarter profit. | Warren E. Buffett promotes the Berkshire Hathaway brand. | Calculating the cost of Ebola | Derivatives change only goes so far."
## [152] "Denis J. McInerney, a former deputy assistant attorney general in the Justice Department, has returned to the law firm Davis Polk & Wardwell."
## [153] "President Obama has decided to wait until after next months midterm elections to nominate a replacement for Attorney General Eric H. Holder Jr., White House officials said."
## [154] "Some of it is OK."
## [155] "Highlights from the International Herald Tribune archives: Nikita S. Khrushchev was believed to be ousted as the Soviet leader in 1964."
## [156] "Hopper Drawing, by Carter E. Foster, was published by the Whitney Museum of American Art in connection with a Hopper exhibition in 2013."
## [157] "Airbnb, the pioneering home rental service, presents itself as useful and virtuous, but the reality is far less benign, according to Attorney General Eric T. Schneiderman of NewYork"
## [158] "The FBI director, James B. Comey, corrected statements he made on 60 Minutes that the bureau did not do electronic surveillance without a court order."
## [159] "Ben C. Solomon is a Times video journalist reporting on Ebola. His video today, about a team of ambulance drivers in Monrovia, Liberia, shows the dangers they face every day."
## [160] "James L. Amine and Timothy P. OHara have been appointed to the Swiss lenders executive board and will lead the investment banking division with Gal de Boissard, the chief executive for Europe, the Middle East and Africa."
## [161] "Food news from Silicon Valley to the UK."
## [162] "The venture capitalist Marc Andreessen is stepping down from eBays board, months after publicly defending his role as a director against attacks from Carl C. Icahn."
## [163] "Best known for his operas and symphonies, Mr. Glasss Piano tudes are the focus of a new recording and concerts at BAM."
## [164] "An SEC case against a high-frequency trading firm shows how difficult it is to draw the line between acceptable trading strategies and manipulation, Peter J. Henning writes in the White Collar Watch column."
## [165] "Vice President Joseph R. Biden Jr. appeared in a hangar near La Guardia Airport on Monday to endorse Gov. Andrew M. Cuomos plans for commissioning a redesign of four airports in and around NewYork City."
## [166] "Senator Patrick J. Leahy, chairman of the Judiciary Committee, is asking Comcast not to engage in paid prioritization of Internet content after its proposed takeover of Time Warner Cable."
## [167] "Tom Steyer, a billionaire hedge fund founder, has become the largest super PAC donor of all time, passing the casino magnate Sheldon G. Adelson."
## [168] "The piano superstar plays the solo cadenza for the first movement of Mozarts Piano Concerto No. 17 in G."
## [169] "If you are wondering why you have been getting fund-raising pitches from the Democratic Congressional Campaign Committee theres at least one explanation: an organization you did provide your email to has rented its list to the WashingtonDCC.C."
## [170] "Through a clever bit of photo manipulation, a new ad by the NRA puts Iowas Democratic Senate nominee, Bruce Braley, next to the man many gun owners consider their nemesis: Michael R. Bloomberg."
## [171] "Photos from SouthAfrica, HongKong, India and the USA."
## [172] "Vice President Joseph R. Biden Jr. beat the comics to the punch line at a recent Kennedy Center appearance."
## [173] "A federal judge ordered a full mental evaluation in the next 30 days of Omar J. Gonzalez, the man accused of jumping the fence and racing past Secret Service officers into the White House with a knife last month."
## [174] "Highlights from the International Herald Tribune archives: French farmers disagreed with General de Gaulles threat to quit the E.E.C. in 1964."
## [175] "The Feds policies that drove down interest rates to historically low levels have actually exacerbated the inequality problem that Janet L. Yellen said concerned her, writes William D. Cohan in Street Scene."
## [176] "Ben Bradlees funeral will be held Tuesday at the Washington National Cathedral, where Richard M. Nixon was remembered in a memorial service in 1994."
## [177] "NewYorkTimess David E. Sanger remembers a conversation with Ben Bradlee about an article in NewYorkTimes."
## [178] "At an investment conference, Scott A. Mather, who now oversees Pimcos Total Return Fund, defends its outsize holdings in European government bonds and other high-yielding securities."
## [179] "Children who drink rice, almond or soy milk instead of cows milk may have insufficient levels of vitamin D."
## [180] "A man later identified as a J.P. Morgan managing director walked into a live, online broadcast of a group of prominent protesters on Wednesday to vent his frustration with the continuing occupation of key locations in HongKong."
## [181] "Gov. Andrew M. Cuomo of NewYork foresees something really, really, really big for Hillary Rodham Clintons future."
## [182] "A hedge fund has emerged as perhaps the last hope for two struggling retails. | Bankers are jockeying for the next sovereign debt deal in Africa, despite plenty of risks. | Shareholders decide today whether to approve Chiquitas plan to acquire an Irish rival. | Charles T. Munger gives to theoretical physics."
## [183] "Richard Norton Smiths biography of Nelson Rockefeller is a portrait of the world when establishment financiers earned political respect, writes Jonathan A. Knee in a book review."
## [184] "A senior administration official confirms that Kathyrn Ruemmler, the former White House counsel, has taken herself out of the running to succeed Eric H. Holder Jr. as attorney general."
## [185] "The festival of theater, dance and performance art presented by P.S. 122 will take place at spaces around the city, while its home on First Avenue is renovated."
## [186] "Ben C. Solomon has been making videos about the Ebola outbreak, finding scenes of courage even as people struggle to contain the disease."
## [187] "Most European banks pass stress test. | Steven Ballmer could claim huge tax benefits on his LosAngeles Clippers deal. | William A. Ackmans outsize bets. | Court ruling disarms shareholders."
## [188] "Arguably the greatest feat of arms in American military history was performed on the Roanoke River at Plymouth, N.C., in the predawn darkness of Oct. 28, 1864."
## [189] "One side threatens to crack down harder; the other side complains about too much enforcement. The question is whether both sides can be satisfied, Peter J. Henning writes in the White Collar Watch column."
## [190] "Plus, El Anatsuis shimmering curtains, Martin Z. Marguliess photography collection and more art events in the week ahead."
## [191] "Vice President Joseph R. Biden campaigns for Representative Bruce Braley in Iowa on Monday."
## [192] "D.J. Tim Sweeneys cult-favorite program turns 15 next year. To celebrate, he has compiled a new double album, a song from which premieres here."
## [193] "William J. Burns, who just stepped down as deputy secretary of state, is being named president of the Carnegie Endowment for International Peace."
## [194] "Timothy D. Cook, Apples chief executive, said that one million credit cards had been activated on Apple Pay in the first three days that the mobile payment system was live."
## [195] "The information of more than 18.5 million California residents was compromised in 2013, according to Kamala D. Harris, Californias attorney general."
## [196] "An Apple executive recently called the design of Xiaomis phones theft. In return, Mr. Barra pointed out that parts of Apples new phones are a bit like handsets from HTC."
## [197] "Elizabeth C. Gorski cooks up a funny midweek puzzle for us."
## [198] "Michael E. Shapiro, who transformed and enlarged the collection of Atlantas High Museum, will leave next year."
## [199] "Sales of Gov. Andrew M. Cuomos memoir fell by more than 43 percent to 535 copies in its second week on shelves."
## [200] "Kering announced a new sustainable fashion program and the C.F.D.A. and Lexus announced the winners for their Eco-Fashion challenge."
## [201] "Quinn Bradlee, a son of Benjamin C. Bradlee, the former executive editor of The Washington Post, rested his head on the coffin after he spoke at his fathers funeral at Washington National Cathedral on Wednesday."
## [202] "He had claimed that the game publisher Activision Blizzard used his image without his permission in Call of Duty: Black Ops II."
## [203] "Senator Angus King, the Maine independent who had backed another independent, Eliot Cutler, for governor, switched his allegiance to the Democrat, Representative Michael H. Michaud."
## [204] "Photos from Turkey, China, India and the USA."
## [205] "Thomas M. Menino, who served as Bostons mayor for 20 years, has died at the age of 71."
## [206] "Neil L. Rudenstine has presided over the library along with Anthony W. Marx, its president during planning for the institutions controversial renovation."
## [207] "Tim Cooks public announcement that he is gay has been met with support, but some point out that the battle for L.G.B.T. equality at work is far from over."
## [208] "David W. Dunlap has worked at NewYorkTimes for 39 years. He recalls the early news coverage of AIDS and compares it to NewYorkTimess coverage of the arrival of the Ebola virus in the USA."
## [209] "Democratic nostalgia seemed to be the theme of the night as former President Bill Clinton campaigned on Thursday for Gov. Andrew M. Cuomo."
## [210] "The French banks results were a turnaround from the previous quarter, when it took a charge of nearly 6 billion euros for legal penalties in the USA."
## [211] "Albert G. Horvath, the Smithsonian Institutions current senior finance official, will serve as secretary until David J. Skorton can take up his permanent role in July."
## [212] "James B. Stewart provides insight into his column about the Apple chief executives announcement that he is proud to be gay."
## [213] "The return of Janet R. Bender."
## [214] "On Tuesday, Michael S. Dell will try to persuade people that his company is about far more than the personal computers and computer servers it has been known for, with products intended for things as varied as the cloud computing networks of global enterprises and handy personal devices."
## [215] "The government has imposed billions of dollars in penalties on the big banks for wrongdoing. But, Peter J. Henning writes in the White Collar Watch column, that may not be enough to change banks behavior."
## [216] "C.H.C.M., the influential Bond Street shop, is debuting an in-house range of refined wardrobe essentials."
## [217] "Photos from Turkey, Syria, India and the USA."
## [218] "Relations between tech companies and the intelligence community has been strained recently, but Adm. Michael S. Rogers played down government concerns."
## [219] "Highlights from the International Herald Tribune archives: Europeans were happy Lyndon B. Johnson was elected in 1964."
## [220] "Millstein & Company, the financial advisory firm founded by James E. Millstein, has hired Mark Walker, a former banker with Rothschild."
## [221] "Despite efforts to improve disclosure under the Freedom of Information Act, the SEC continues to resist sharing data with the public, William D. Cohan writes in the Street Scene column."
## [222] "William A. Ackmans hedge fund could vote its stake in Allergan in support of ousting directors at a meeting scheduled for Dec. 18, bolstering his effort to force the company into a sale."
## [223] "Some post-shellacking advice for the POTUS."
## [224] "Photos from Israel, Algeria, Russia and the USA."
## [225] "John L. Thornton, the chairman of Barrick Gold and a professor at Tsinghua University in Beijing, was designated as a possible successor to Henry M. Paulson Jr. when they were at Goldman Sachs."
## [226] "A. Jerrold Perenchio, the former chairman of Univision, pledged to donate about 50 artworks including notable Impressionist paintings "
## [227] "Rita Doves poem November for Beginners, and the article Heavy Autumn Snowstorm Barrels Across Northeast, by Al Baker, Elizabeth A. Harris and Sarah Maslin Nir, appear in this pairing."
## [228] "An emboldened Speaker John A. Boehner warned President Obama against unilateral action to overhaul the immigration system."
## [229] "The 82 year old has been recording with her daughter, Patsy L. Russell, and John Carter Cash, for a project combining new songs and old folk and gospel tunes."
## [230] "The designation of MetLife as systemically important is a sign that regulators are looking ahead to the next possible crisis, Stephen J. Lubben writes in the In Debt column."
## [231] "The Democratic Senatorial Campaign Committee cancels $2 million worth of ads on behalf of Senator Mary L. Landrieu of Louisiana."
## [232] "Photos from Ukraine, Syria, Scotland and the USA."
## [233] "Barry C. Silk writes puzzles for grown-ups, but once in a while you can find stuff for less mature adults like me."
## [234] "The Sony Pictures Classics co-president will take over from Herbert S. Schlosser, who will be chairman emeritus."
## [235] "Vice President Joseph R. Biden Jr. spoke quickly on his relationship with Prime Minister Benjamin Netanyahu of Israel during remarks to the Jewish Federation."
## [236] "Mr. Morrison will play J.M. Barrie in the show about the creation of Peter Pan; he and Jeremy Jordan have both played the role in earlier productions."
## [237] "The term political correctness is perhaps most often used by its detractors but new research has found that thinking about being P.C. can actually improve creativity."
## [238] "Senator Patrick J. Leahy said he believes Loretta E. Lynch, President Obamas choice for attorney general, should be confirmed easily no matter when the Senate votes."
## [239] "By analyzing samples from each of the five NewYork City boroughs, Jane M. Carlton hopes to detect viruses before an outbreak."
## [240] "Is the food label \"natural\" misleading to consumers? Should the F.D.A. enforce a definition of the term?"
## [241] "Another small sampling of sparkling prose in recent editions, from serious to silly to Kenny G."
## [242] "President Obama and President Vladimir V. Putin of Russia discussed Iran, Syria and Ukraine during three brief conversations at the Asia-Pacific Economic Cooperation summit meeting."
## [243] "Andrea C. Bonomi, the Italian businessman battling a Chinese French bid for Club Mditerrane, announces a sweetened offer for the resort company."
## [244] "Reporters were surprised on Tuesday morning when the Federalist Society, a conservative legal group, announced that the news media would be barred from an appearance by Justice Samuel A. Alito Jr. at its annual gala black-tie dinner."
## [245] "Heaven, the first offering from the Birmingham raised, LosAngeles-based 24 year old T.O.L.D., premieres here."
## [246] "The activist investor William A. Ackman has acquired an 8.5 percent economic interest in the former animal health arm of Pfizer."
## [247] "Photos from France, China, Poland and the USA."
## [248] "Both steps fall short of the big breakup of Dow Chemical being advocated by the activist hedge fund manager Daniel S. Loeb."
## [249] "Mr. Grammer is to play the largely comic role of Charles Frohman, the theater producer of Peter Pan playwright J.M. Barrie."
## [250] "Photos from Turkey, HongKong, Afghanistan and the USA."
## [251] "Steven A. Ballmer, Microsofts former chief executive, is making a financial contribution that will enable the school to expand its computer science faculty by about 50 percent."
## [252] "Dassault Systmes, a French company, has a five year research agreement with the F.D.A. focused on using 3 D technology for simulating the reliability of pacemaker wires."
## [253] "Its in the bullet points: Shoes, bags, more mobile and social media, and a focus on the USA."
## [254] "Berkshire Hathaway, the conglomerate run by Warren E. Buffett, will acquire Duracell using a transaction aimed at lowering the tax bill."
## [255] "President Obama and Prime Minister Dmitri A. Medvedev of Russia at the East Asia summit plenary session in Naypyidaw, Myanmar, on Thursday."
## [256] "Readers wrote about a public reckoning for our wars in Iraq and Afghanistan and the sacrifices of our troops in comments on Daniel P. Bolgers Op-Ed, The Truth About the Wars."
## [257] "Warren E. Buffett has an appetite for large and creatively structured acquisitions. A look back at some of his recent deals."
## [258] "Thomas O. Kriegsmann is to be its director of programs, a new position that will include artist development projects."
## [259] "David W. Dunlap, a Metro reporter for NewYorkTimes, took us back to the moment when the paper stopped being produced with Linotype machines."
## [260] "Daniel S. Loeb, the activist hedge fund manager, has been pressing for a breakup of Dow Chemical and has been rebuffed in his attempt to name two directors."
## [261] "More than $40 million has been raised in honor of Joan H. Weill, who is stepping down as chairwoman of the Alvin Ailey Dance Foundation."
## [262] "Net neutrality could give Comcast a way out of its Time Warner Cable acquisition. | Warren E. Buffetts deal for Duracell satisfies a number of his deal-making preferences. | Virgin America to begin trading. | Goldman seeks to lure tech talent."
## [263] "Evan R. Chesler, the chairman of the law firm Cravath, Swaine & Moore, will take over as the library chairman from Neil L. Rudenstine."
## [264] "Finish Big and Succession, examine how entrepreneurs and corporate chieftains alike can be assessed on their departures, Jonathan A. Knee writes in a joint book review."
## [265] "Senator Mary L. Landrieus campaign has not hesitated to embrace her stance on the XL Pipeline ahead of her runoff election against Representative Bill Cassidy."
## [266] "Celebrities should not quit their day jobs, according to Andrew J. Ries."
## [267] "And what about that I.S.I. dreamboat Aasar Khan?"
## [268] "The alliance between the American and German companies is expected to strengthen Mercks oncology business in several important markets, including the USA."
## [269] "The animal health care company Zoetis, which has attracted the interest of the deal maker William A. Ackman, has made a deal of its own."
## [270] "The $66 billion acquisition by Actavis would be the largest deal this year and would thwart the takeover effort of Valeant and William A. Ackman."
## [271] "Representative Nancy Pelosi has a birthday wish for Speaker John A. Boehner."
## [272] "Insider trading cases are casting a spotlight on whether courts should defer to administrative agencies in determining criminal violations, Peter J. Henning writes in the White Collar Watch column."
## [273] "Representative Steve Israel joked about handing off the WashingtonDCC.C. chairmanship."
## [274] "Mayor Francis G. Slay said troops operating within the city of SaintLouis would have a secondary role in responding to any protests, and that police officers would be the ones dealing directly with demonstrators."
## [275] "What do you do when you are a billionaire and a movie buff? If you are Paul G. Allen, you turn the iconic theater you already own into a state-of-the-art venue."
## [276] "William A. Ackman and Valeant Pharmaceuticals lost their hostile battle to buy Allergan and still came away with a total of $2.6 billion."
## [277] "WallStreet is on pace for a big year of deal-making. | William A. Ackman may have lost Allergan, but he still came away a winner. | The Justice Department is weighing a civil suit against Angelo R. Mozilo, the former chief executive of Countrywide Financial. | USA plans second Bitcoin auction."
## [278] "A briefing book for the White House chief of staff, Denis R. McDonough, had a note from one of his children attached to it as he sat in a meeting on Ebola in the White House on Tuesday."
## [279] "Much like their leader, Warren Buffett, the chief executives of Berkshire Hathaways subsidiaries are a savvy group of acquirers. Lawrence A. Cunningham, in the Another View column, explains why."
## [280] "American Ballet Theater and the Segerstrom Center for the Arts in Costa Mesa, Calif., will open the American Ballet Theater William J. Gillespie School on the Segerstrom campus in September, both organizations announced on Tuesday."
## [281] "Assembling the tale of G.M.s faulty ignition switch, which led to the deaths of at least 13 people, took months of digging by a team of reporters and editors."
## [282] "Joseph P. Clancy, acting director of the Secret Service, is to testify before Congress."
## [283] "Thomas B. Leonardi will be joining the investment bank as a senior adviser, focusing on the insurance industry."
## [284] "The release of an interview Robert Rubin gave nine years ago offers a window into the thoughts of the rich and powerful, William D. Cohan writes in the Street Scene column."
## [285] "On Saturday, the Arcangel-composed series Dances for the Electric Piano will be staged for the first time in the USA. Listen to it here."
## [286] "Representative Paul D. Ryan, the new chairman of the Ways and Means Committee, said that a broad overhaul of the tax code depended on the administrations engagement."
## [287] "Gov. Bobby Jindal of Louisiana tried to cut off an immigration discussion with Chuck Todd of NBC."
## [288] "The country plans to scrap its state monopoly on the sale of salt, a move that would mark the end of a system that can be traced back to 685 B.C."
## [289] "Despite being about to receive his Ph.D. in physics from Harvard, Kevin Niu plans to eschew the academic life to follow his calling in the entertainment industry."
## [290] "Daniel K. Tarullo, the Fed governor, described to a Senate subcommittee potential rules that could restrict banks from some types of commodities operations."
## [291] "Speaker John A. Boehner addressed President Obamas immigration actions."
## [292] "Is the Federal Reserve up to the task of regulating financial institutions that are so large and complex? the chairman of a Senate panel asked William C. Dudley."
## [293] "Attorney General Eric H. Holder Jr. offered guidance to law enforcement agencies and urged protesters to be restrained."
## [294] "Vice President Joseph R. Biden Jr. had to beat a hasty retreat on Friday at the sight of an unruly crowd of Ukrainians in Kiev protesting the new government amid continuing unrest in that former Soviet republic."
## [295] "Paul J. Taubman is bringing Don Cornwell, a managing director who specializes in sports team deals, to his boutique investment bank."
## [296] "At noon on Friday a handful of people performed a mock lynching across the street from the Old Courthouse in SaintLouis as a silent protest of the police killings of Michael Brown and Vonderrit D. Myers Jr."
## [297] "Last week was a rough one for the Federal Reserve. | Paul J. Taubman continues to poach talent from Morgan Stanley, his former employer. | Mathew Martoma has started his nine year prison term for insider trading."
## [298] "The Chapter 11 filing by Aereo prompts a larger question of whether bankruptcy courts should actively police debtors motives for filing cases, writes Stephen J. Lubben in the In Debt column."
## [299] "The Fed has to balance the conflicting roles it plays as an overseer of the banks and as a law enforcement agency pursuing misconduct, Peter J. Henning writes in the White Collar Watch column."
## [300] "Speaker John A. Boehner announced that he was reappointing Representative Trey Gowdy, Republican of SouthCarolina, as chairman of the special committee on Benghazi for the 114th Congress that begins in January."
## [301] "Senator Elizabeth Warrens wrath toward Antonio F. Weiss is misdirected, and her understanding of the inversion deal on which she bases her opposition appears misinformed."
## [302] "Senator Charles E. Schumer said Tuesday that it was a political mistake to pass the Affordable Care Act in 2010 because voters at the time were looking for relief from the recession not universal health care."
## [303] "Photos from Egypt, HongKong, Kenya and the USA."
## [304] "The return of Michael S. Maurer."
## [305] "Thanksgiving was my Korean familys annual recommitment ceremony to the USA."
## [306] "The potential political costs of the civil unrest in Ferguson, Mo.; speculation about Chuck Hagels replacement; and a look at Speaker John A. Boehners turkey brine recipe."
## [307] "StevenACohens ex-wife gets outside financing for her lawsuit. | Poison puts develop a downside. | Trustee moves to repay Mt. Gox creditors. | Philip A. Falcone to Depart the Harbinger Group."
## [308] "Photos from HongKong, China, Syria and the USA."
## [309] "In Newport, R.I., visitors can tour storied mansions decorated for Christmas."
## [310] "Michael B. Jordan and John Turturro will take part in a reading of the script for Spike Lees Do the Right Thing"
## [311] "David W. Dunlap, a Metro reporter and the writer of the Building Blocks column, explored the sometimes close connection between Mr. Castro and NewYorkTimes."
## [312] "A wide open grid from Elizabeth C. Gorski."
## [313] "Celebrate the holidays with Elizabeth C. Gorski."
## [314] "Programs, and payouts, to encourage whistle-blowers to come forward with wrongdoing are likely to increase with the new Congress, Peter J. Henning writes."
## [315] "Jeh C. Johnson, the Homeland Security secretary, is prepared to defend the presidents decision to take executive action to overhaul the immigration system."
## [316] "Photos from HongKong, Paris, SouthKorea and the USA."
## [317] "The reporters J. David Goodman and Michael Wilson described a suburban mothers descent to a heroin addict whose home on Staten Island served as a drug-dealing center."
## [318] "Chief Justice John G. Roberts Jr. is well known for his ability to quote obscure case law, but on Monday he invoked the rapper Eminem at the Supreme Court."
## [319] "Mary L. Landrieu, a three term senator, is in the race of her career against the Republican Bill Cassidy as Louisiana voters head to the polls for a runoff vote on Dec. 6."
## [320] "Bank of NewYork Mellon said on Tuesday that it would give a board seat to Edward P. Garden of the hedge fund Trian Fund Management, forestalling a potentially costly proxy fight."
## [321] "The deal for IndCor Properties will make Singapores sovereign wealth fund a major owner of warehouses and distribution centers in the USA."
## [322] "Speaker John A. Boehner on options that Republicans are considering to block President Obamas immigration action."
## [323] "Senator Joseph R. McCarthy was censured 60 years ago today."
## [324] "Jonah M. Kessel filmed a documentary video in China and Myanmar, where jade mines have become a free market for heroin, and tells of the story of an addict he encountered."
## [325] "In a spellbinding letter to Jim Cramer, the activist investor J. Carlo Cannell urged him to cut his pay 70 percent, resign from CNBC and direct his energy to helping your fellow shareholders crawl back from Hades."
## [326] "The Fox affiliate in NewOrleans is taking issue with the use of its news anchors words in a campaign ad for Senator Mary L. Landrieu."
## [327] "NewYorks chokehold case has thrown a volatile element into Loretta E. Lynchs confirmation hearings as USA attorney general."
## [328] "Senator Elizabeth Warrens opposition to a nominee for a role at the Treasury Department demonstrates her blatant political motivation, William D. Cohan writes in the Street Scene column."
## [329] "J. Carlo Cannell wants Jim Cramer to sell TheStreet or take a pay cut and quit CNBC. A sale may be wishful thinking, says Quentin Webb of Reuters Breakingviews. But Mr. Cramers payout is hard to justify."
## [330] "The appellate division of the NewYork State Supreme Court dismissed a lawsuit at the heart of a bitter dispute between the billionaire collector Ronald O. Perelman and the art dealer Larry Gagosian."
## [331] "Photos from Russia, SouthAfrica, India and the USA."
## [332] "Andrea C. Bonomi, the Italian businessman fighting a Chinese investor for Club Mditerrane, raised his bid to $29.65 a share for the French resort operator."
## [333] "President Obama on Friday announced his selection of Ashton B. Carter to lead the Pentagon, White House officials said, embracing a physicist and national security centrist who may advocate a stronger use of American power."
## [334] "Jon Caramanica and A.O. Scott discuss what this update of The Bodyguard gets right about the music business."
## [335] "The famously outspoken, leather-clad architect is being honored this week with Design Miamis inaugural Design Visionary Award and an exhibition at the Bass Museum of Art. In this video, he discusses his career with T."
## [336] "An architect who hopes to save a Paul Rudolph-designed building in Orange County, N.Y., presented his detailed proposal to county leaders there on Friday."
## [337] "Demonstrations focused on fatal police encounters continued on Saturday, with a rally at the Louis H. Pink Houses in Brooklyn to protest the fatal shooting of Akai Gurley there and a grand jurys decision not to indict an officer in the death of Eric Garner."
## [338] "Will the SECs treatment of Bank of America, Peter J. Henning asks in the White Collar Watch column, become the norm for other banks seeking waivers from rules on bad actors?"
## [339] "After years of research, there is genuine hope for reducing the toll exacted by lung cancer, which is the leading cause of cancer deaths in the USA."
## [340] "Senator Charles E. Schumer of NewYork may be Republicans favorite Democrat these days."
## [341] "Recommendations from the American Bankruptcy Institute failed to address derivatives held by nonfinancial institutions that will keep operating, writes Stephen J. Lubben in the In Debt column."
## [342] "Readers expressed outrage and despair in comments on a Friday editorial, It Wasnt Just the Chokehold: Eric Garner, Daniel Pantaleo and Lethal Police Tactics, and on an Op-Ed from Eric L. Adams, We Must Stop Abuse of Black Men."
## [343] "Jessica Bibliowicz will become chairwoman of the Cornell Weill Medical College, effective Jan. 1. Her father, Sanford I. Weill, has been chairman for two decades."
## [344] "The SecondMarket exchange won all but one of the 20 Bitcoin blocks in the governments auction. The venture capitalist Timothy C. Draper, who swept the first auction, won only 2,000 Bitcoins this time."
## [345] "Brian T. Moynihan tells a conference that his bank is expecting trading revenue in the fourth quarter to be down from last quarter and from a year earlier."
## [346] "The revival of A.R. Gurneys play will close this Sunday, before some of its scheduled stars ever get to perform."
## [347] "Jonathan Gruber, the M.IT economist who advised the Obama administration on the Affordable Care Act, apologized on Tuesday for inflammatory comments that have brought negative attention to the law in recent months."
## [348] "The Senate Intelligence Committee has released its long-awaited review on the torture of prisoners held by the Central Intelligence Agency during the George W. Bush administration."
## [349] "Senator Dianne Feinstein, Democrat of California and chair of the Senate Intelligence Committee, headed to the Senate floor Tuesday to talk about the committees newly released report on the torture of prisoners held by the Central Intelligence Agency under the George W. Bush administration."
## [350] "Several Republican members of the Senate Intelligence Committee pushed back against the release of the declassified executive summary of the panels report on the torture of prisoners by the Central Intelligence Agency. They condemned the report on techniques used during the George W. Bush administration as partisan."
## [351] "Just hours after announcing its $8.4 billion acquisition of Cubist Pharmaceuticals, a Delaware judge invalidated patents owned by the antibiotics maker. Merck appears to have been caught up in the M.&.A. exuberance, says Robert Cyran of Reuters Breakingviews."
## [352] "Recent criticism of James J. Cramers compensation from TheStreet.com has painted him as another greedy WallStreet executive looking out for himself but not shareholders."
## [353] "Gov. Chris Christie of NewJersey, who was appointed USA attorney for the state by President George W. Bush in the weeks after the Sept. 11 attacks, seemed not eager on Wednesday to discuss the Senate report on the brutal American interrogation techniques used on terrorism suspects a decade ago."
## [354] "In this lesson we offer three different teaching ideas to engage students in the debate over immigration policy in the USA."
## [355] "The latest appeals court decision will make some insider-trading cases harder, but it does not give a free pass to hedge funds to trade on any confidential information, Peter J. Henning writes in White Collar Watch."
## [356] "The latest NewYorkTimes poll is based on telephone interviews conducted Dec. 4 through 7 with 1,006 adults throughout the USA."
## [357] "The Confucius Peace Prize was first given out in late 2010 as a rejoinder to the Nobel Peace Prize. President Vladimir V. Putin of Russia is a past recipient."
## [358] "Were going to continue to press his nomination forward, Treasury Secretary Jacob J. Lew said. He also spoke about corporate taxes and sanctions on Russia."
## [359] "George R. Goldner will become a private art adviser after 21 years heading the department. Nadine M. Orensteinhas been named to succeed him."
## [360] "Lloyd C. Blankfein, Goldman Sachss chief executive, addressed the antagonism that has been coming out of Washington."
## [361] "Francis J. Shammo, Verizons chief financial officer, said the company planned to continue to invest in its FiOS fiber-optic network and its wireless systems regardless of the outcome of the broadband debate."
## [362] "Senator Dianne Feinstein, chairwoman of the committee that investigated the CIAs interrogation program, offered a live rebuttal as John O. Brennan, the agencys director, responded to the committees findings."
## [363] "The Blackstone Group, the private equity giant run by Stephen A. Schwarzman, is almost finished raising a new fund for energy investments that is expected to exceed $4 billion in assets."
## [364] "OscarSSchafer will succeed the current NewYork Philharmonic chairman, Gary W. Parr, early next year."
## [365] "Friday and the weekend: a struggle for L.G.B.T. protections, more chilly weather, protests on Saturday, and the week in pictures."
## [366] "An A.C.L.U. video features military personnel who objected to the Bush-era torture program."
## [367] "Two new books provide insight into lesser-known stages of Charles M. Schulz and Jack Kirbys careers."
## [368] "On this day in 2000, the Supreme Court ruled 5 to 4 that there would be no additional counting of presidential votes in Florida. 35 days after Election Day, the decision effectively handed the presidency to George W. Bush."
## [369] "Hours after casting his final vote as a member of Congress, Representative John D. Dingell was hospitalized after taking a spill earlier in the week."
## [370] "Jungle Lady is the first single off the NewYork-based act Lion Babes self-titled debut EP."
## [371] "The Securities and Exchange Commission said it would not be able to proceed with the lawsuit because the two main witnesses it intended to call to testify are in Poland and had no plans to return to the USA."
## [372] "The return of Joel D. Lafargue, after a long break."
## [373] "Nominees that didnt make the final cut this year include Nine Inch Nails, Sting, N.W.A, Chic and Kraftwerk."
## [374] "The disturbing statistics on military rape and the fleeting attention of the public led Mary F. Calvert to document the stories of those who survived assaults."
## [375] "James E. Staley, a managing partner at the NewYork hedge fund BlueMountain Capital Management, is expected to be considered for the board during UBSs annual meeting in May, and to join the banks risk committee."
## [376] "Wednesday: The M.T.A. honors its workers, oddly warm weather, and a vigil for Pakistan."
## [377] "President Obama is scheduled to speak about relations with Cuba at 12 p.m. ET."
## [378] "Highlights and video of the remarks by President Raul Castro of Cuba on a diplomatic breakthrough with the USA."
## [379] "Speaker John A. Boehner on Wednesday called President Obamas new policy on Cuba the latest in a long line of mindless concessions to a dictatorship that brutalizes its people and schemed with our enemies."
## [380] "Alan P. Gross prepared his remarks at his attorneys office in Washington on Wednesday."
## [381] "Most Americans support normalizing trade and diplomatic relations with Cuba and consider what happens in the communist nation to be important to the interests of the USA."
## [382] "Photos from Pakistan, Australia, France and the USA."
## [383] "President Obama took the occasion of Alan P. Grosss release not only to acknowledge the interest the Jewish community had taken in Mr. Grosss case, but also to connect his release to the holiday and to Jewish teaching."
## [384] "Curtis L. Buser, who has been interim finance chief for the last seven months, is taking over the job permanently."
## [385] "Speaker John A. Boehner posted a holiday greeting on Thursday, a video poem title Happy Christmas to All, although the Speakers wishes may not extend all the way to the White House."
## [386] "Recommended books for learning more about Cubas history and its relationship to the USA."
## [387] "In a speech on Thursday, Benjamin M. Lawsky, NewYork States top financial regulator, provided the most detail yet on revisions to his proposed Bitcoin rules."
## [388] "The firm promoted John E. Waldron, one of its star deal makers, to become a co-head of its investment bank, succeeding a scion of one of Goldmans most famous families."
## [389] "The disposal is the latest move for Xerox, which has been transformed under the leadership of Ursula M. Burns into a provider of various business services."
## [390] "Speaker John A. Boehner has invited President Obama to address the country on Jan. 20 and give a State of the Union speech before a joint session of the new Republican-controlled Congress."
## [391] "Mark A. Flaherty and Mark O. Winkelman will serve on a board that now has 14 members."
## [392] "Goldman names John E. Waldron co-head of its investment bank. | Regulators deem MetLife too big to fail. | Another big whistle-blower reward in Bank of America case. | London tenants win battle over USA equity firm."
## [393] "After an investigation into conflicts of interest, William C. Erbey has agreed to step down from his position as chairman of Ocwen and four other related companies."
## [394] "Its about non-digital IT."
## [395] "Fred R. Conrad may be best known for his exquisite portraits, but an assignment in Kosovo taught him the value of watching and waiting for the story to come to him."
## [396] "An overhaul of the Freedom of Information Act did not survive the legislative deal-making that produced the spending plan. William D. Cohan laments that loss in the Street Scene column."
## [397] "Its hard to be FDR."
## [398] "Ocwen should look at Clayton Homes, whose credo puts customers first, writes Lawrence A. Cunningham in Another View."
## [399] "Photos from Ukraine, India, Nepal and the USA."
## [400] "NewYorkTimes reporter David W. Dunlap explains how coverage of the Bay of Pigs disaster unfolded."
## [401] "After decades of oppression of people who identify as L.G.B.T., the Cuban government now pays for gender reassignment surgery."
## [402] "Look for the hidden message from Peter A. Collins."
## [403] "The new year will bring cases from the subprime mortgage crisis, a Justice Department decision on an appeal of an insider trading ruling and, perhaps, new accounting frauds, Peter J. Henning writes in the White Collar Watch column."
## [404] "Under the F.D.A.s new policy, any man who has had sex with another man in the last year may not donate."
## [405] "Representative Michael G. Grimm of NewYork insisted last week that he would not resign from his seat. On Monday night, he changed his mind."
## [406] "Speaker John A. Boehner gave his seal of approval to Representative Michael Grimms resignation."
## [407] "David E. Sanger, chief Washington correspondent for NewYorkTimes, recalled a visit to the country 25 years ago."
## [408] "Former President George H. W. Bush was released from a Houston hospital where he has been staying since last week after experiencing shortness of breath."
## [409] "Speaker John A. Boehner expressed his support for Representative Steve Scalise, who spoke at a conference for white supremacists in 2002."
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in Headline: "
## pattern .n
## 1 New Album 6
## 2 New Capital 4
## 3 New Director 4
## 4 New App 3
## 5 New Chief 3
## 6 New Creative 3
## 7 New Gig 3
## 8 New Musical 3
## 9 New President 3
## 10 New British 2
## 11 New Building 2
## 12 New Businesses 2
## 13 New Course 2
## 14 New Dance 2
## 15 New Edition 2
## 16 New Financing 2
## 17 New Firm 2
## 18 New Food 2
## 19 New GOP 2
## 20 New Head 2
## 21 New Hotel 2
## 22 New Inversion 2
## 23 New Life 2
## 24 New Light 2
## 25 New Listing 2
## 26 New Look 2
## 27 New One 2
## 28 New Plan 2
## 29 New Restaurant 2
## 30 New Rules 2
## 31 New Season 2
## 32 New Study 2
## 33 New Thing 2
## 34 New Tool 2
## 35 New Video 2
## 36 New Website 2
## 40 New Ad 1
## 41 New Adele 1
## 42 New Afghan 1
## 43 New Again 1
## 44 New Airport 1
## 45 New Albert 1
## 46 New Albums 1
## 47 New Answers 1
## 48 New Anti 1
## 49 New Apple 1
## 50 New Approach 1
## 51 New Arrival 1
## 52 New Arrivals 1
## 53 New Audience 1
## 54 New Autobiographies 1
## 55 New Autocorrect 1
## 56 New Badges 1
## 57 New Bag 1
## 58 New Band 1
## 59 New Banknotes 1
## 60 New Bears 1
## 61 New Big 1
## 62 New Bike 1
## 63 New Bill 1
## 64 New Biotech 1
## 65 New Birth 1
## 66 New Black 1
## 67 New Bond 1
## 68 New Book 1
## 69 New Broadway 1
## 70 New Broker 1
## 71 New Brooklyn 1
## 73 New Campaign 1
## 74 New Candidate 1
## 75 New Carpetbagger 1
## 76 New Cash 1
## 77 New Cause 1
## 78 New Chairman 1
## 79 New Charity 1
## 80 New Chart 1
## 81 New Chinese 1
## 82 New Classical 1
## 83 New Climate 1
## 84 New Club 1
## 85 New Co 1
## 72 New CO2 1
## 86 New Conflict 1
## 87 New Contemporary 1
## 88 New Crime 1
## 89 New Crowdsourced 1
## 90 New Crowns 1
## 91 New Culture 1
## 92 New Dealer 1
## 93 New Default 1
## 94 New Dell 1
## 95 New Design 1
## 96 New Designer 1
## 97 New Devices 1
## 98 New Dinosaur 1
## 99 New Diplomacy 1
## 100 New Downtown 1
## 101 New Duo 1
## 102 New Ensemble 1
## 103 New Entrant 1
## 104 New Event 1
## 105 New Exhibition 1
## 106 New Exhibitions 1
## 107 New FAA 1
## 108 New Face 1
## 109 New Ferguson 1
## 110 New Film 1
## 111 New Flights 1
## 112 New Floral 1
## 113 New Fordham 1
## 114 New Friends 1
## 115 New Front 1
## 116 New Fund 1
## 117 New Fundraising 1
## 118 New Galaxy 1
## 119 New Gender 1
## 120 New Guide 1
## 121 New Guidelines 1
## 122 New Halloween 1
## 123 New Hard 1
## 124 New Haunt 1
## 125 New Healing 1
## 126 New Health 1
## 127 New High 1
## 128 New Historical 1
## 129 New Hit 1
## 130 New Holiday 1
## 131 New Home 1
## 132 New Ideas 1
## 133 New Image 1
## 134 New Information 1
## 135 New Institute 1
## 136 New Japanese 1
## 137 New Jonathan 1
## 138 New Journalism 1
## 139 New Kennedy 1
## 140 New Kentucky 1
## 141 New Kids 1
## 142 New Kind 1
## 143 New LaBute 1
## 144 New Laura 1
## 145 New Leader 1
## 146 New Leaders 1
## 147 New Lease 1
## 148 New Level 1
## 149 New Loretta 1
## 150 New Lucerne 1
## 152 New Madonna 1
## 153 New Mamet 1
## 154 New Manager 1
## 155 New Marching 1
## 156 New Margaret 1
## 157 New Market 1
## 158 New Marylebone 1
## 159 New Measurements 1
## 160 New Messaging 1
## 161 New Mortgage 1
## 151 New MSG 1
## 162 New Multimedia 1
## 163 New Museum 1
## 164 New Names 1
## 165 New Nations 1
## 166 New Neutral 1
## 167 New Nick 1
## 168 New NorthAmerican 1
## 169 New Opposition 1
## 170 New Outpost 1
## 171 New Paper 1
## 172 New Partners 1
## 173 New Performance 1
## 174 New Pet 1
## 175 New Philanthropy 1
## 176 New Pizzeria 1
## 177 New Policy 1
## 178 New Poll 1
## 179 New Polls 1
## 180 New Pork 1
## 181 New Prime 1
## 182 New Privacy 1
## 183 New Product 1
## 184 New Productions 1
## 185 New Proposal 1
## 186 New Publishing 1
## 187 New Pumpkin 1
## 188 New Releases 1
## 189 New Research 1
## 190 New Resource 1
## 191 New Road 1
## 192 New Role 1
## 193 New Rule 1
## 195 New Sales 1
## 194 New SBA 1
## 196 New Scrutiny 1
## 197 New Series 1
## 198 New Shoe 1
## 199 New Short 1
## 200 New Show 1
## 201 New Site 1
## 202 New Smartphones 1
## 203 New Smog 1
## 204 New Sports 1
## 205 New Stadium 1
## 206 New Stands 1
## 207 New Starring 1
## 208 New Start 1
## 209 New Stephen 1
## 210 New Steps 1
## 211 New Studies 1
## 212 New Subscription 1
## 213 New Supporters 1
## 214 New Tablet 1
## 215 New Techniques 1
## 216 New Technologies 1
## 217 New Things 1
## 218 New Thoughts 1
## 219 New Tiananmen 1
## 220 New Tina 1
## 221 New Tome 1
## 222 New Track 1
## 224 New Unit 1
## 225 New Unofficial 1
## 223 New USA 1
## 226 New Views 1
## 227 New Wave 1
## 228 New Way 1
## 229 New Ways 1
## 230 New Wedge 1
## 231 New Whitney 1
## 232 New Work 1
## 233 New Worlds 1
## 234 New Wreaths 1
## 239 Saint-Gobain 1
## 240 St. Ann 1
## 241 St. Baldrick 1
## 242 St. Peter 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in Snippet: "
## pattern .n
## 2 New Atheist 1
## 3 New Brain 1
## 4 New Centurys 1
## 5 New Cuban 1
## 6 New Deal 1
## 7 New Enterprise 1
## 8 New Era 1
## 9 New Rich 1
## 10 New Things 1
## 27 Saint-Gobain 1
## 28 St. Baldricks 1
## 29 St. Mary 1
## 30 St. Y 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in Abstract: "
## pattern .n
## 2 New Atheist 1
## 3 New Brain 1
## 4 New Centurys 1
## 5 New Cuban 1
## 6 New Deal 1
## 7 New Enterprise 1
## 8 New Era 1
## 9 New Rich 1
## 10 New Things 1
## 27 Saint-Gobain 1
## 28 St. Andrew 1
## 29 St. Baldricks 1
## 30 St. Mary 1
## 31 St. Y 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in Headline: "
## pattern .n
## 1 C Card 1
## 2 E and 1
## 3 N Cheez 1
## 4 N Weaver 1
## 5 W Hotels 1
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in Snippet: "
## pattern .n
## 1 N.Y 5
## 2 C Concourse 1
## 3 C.F 1
## 4 C.H 1
## 5 C.L 1
## 6 C.M 1
## 7 E and 1
## 8 E is 1
## 9 E.E 1
## 10 N.C 1
## 11 N.W 1
## 12 S and 1
## 13 S.I 1
## 14 S.S 1
## 15 W Hotels 1
## 16 W Retreat 1
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in Abstract: "
## pattern .n
## 1 N.Y 5
## 2 C Concourse 1
## 3 C.F 1
## 4 C.H 1
## 5 C.L 1
## 6 C.M 1
## 7 E and 1
## 8 E is 1
## 9 E.E 1
## 10 N.C 1
## 11 N.W 1
## 12 S and 1
## 13 S.I 1
## 14 S.S 1
## 15 W Hotels 1
## 16 W Retreat 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in Headline: "
## pattern .n
## 1 Central Bank 3
## 2 East Side 3
## 3 North West 2
## 4 West to 2
## 5 Central Bankers 1
## 6 Central Cast 1
## 7 Central Italy 1
## 8 Central Role 1
## 9 East Africa 1
## 10 East Berlin 1
## 11 East London 1
## 12 East Poland 1
## 13 East Rivals 1
## 14 East Spring 1
## 15 North Transfer 1
## 16 North Views 1
## 17 South Lawn 1
## 20 South of 1
## 18 South Pacific 1
## 19 South Street 1
## 21 South to 1
## 22 West 11th 1
## 23 West Africa 1
## 24 West African 1
## 25 West Coast 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in Snippet: "
## pattern .n
## 1 East Side 12
## 2 Central Bank 8
## 3 West Africa 8
## 4 Central Intelligence 5
## 5 East Coast 5
## 6 West Bank 5
## 7 East and 4
## 8 West Coast 4
## 9 South Beach 3
## 10 West Side 3
## 11 East End 2
## 12 North Portico 2
## 13 South Africas 2
## 14 West African 2
## 16 West and 2
## 15 West Village 2
## 23 Central and 1
## 24 Central bankers 1
## 17 Central Banks 1
## 25 Central business 1
## 18 Central District 1
## 26 Central show 1
## 19 Central Television 1
## 20 Central Terminal 1
## 21 Central Time 1
## 22 Central Valley 1
## 27 East 55th 1
## 28 East Africa 1
## 29 East Asia 1
## 30 East Berliners 1
## 31 East Christians 1
## 41 East correspondent 1
## 32 East Flatbush 1
## 33 East German 1
## 34 East Germans 1
## 42 East haunts 1
## 35 East Hollywood 1
## 43 East in 1
## 36 East Jerusalem 1
## 37 East Poland 1
## 38 East River 1
## 39 East Room 1
## 40 East Timor 1
## 44 North African 1
## 45 North Americas 1
## 46 North Carolinas 1
## 47 North English 1
## 50 North gates 1
## 51 North to 1
## 48 North Vietnamese 1
## 49 North Water 1
## 58 South as 1
## 52 South Asia 1
## 53 South Asian 1
## 54 South Dakotas 1
## 55 South Florida 1
## 59 South in 1
## 60 South of 1
## 56 South Rhodesias 1
## 57 South Will 1
## 61 West 47th 1
## 62 West 50th 1
## 63 West Florrissant 1
## 67 West in 1
## 68 West is 1
## 64 West Rivers 1
## 65 West Sider 1
## 66 West Wing 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in Abstract: "
## pattern .n
## 1 East Side 12
## 2 Central Bank 8
## 3 West Africa 8
## 4 Central Intelligence 6
## 5 East Coast 5
## 6 West Bank 5
## 7 East and 4
## 8 West Coast 4
## 9 South Beach 3
## 10 West Side 3
## 11 East End 2
## 12 North Portico 2
## 13 South Africas 2
## 14 West African 2
## 16 West and 2
## 15 West Village 2
## 23 Central and 1
## 24 Central bankers 1
## 17 Central Banks 1
## 25 Central business 1
## 18 Central District 1
## 26 Central show 1
## 19 Central Television 1
## 20 Central Terminal 1
## 21 Central Time 1
## 22 Central Valley 1
## 27 East 55th 1
## 28 East Africa 1
## 29 East Asia 1
## 30 East Berliners 1
## 31 East Christians 1
## 41 East correspondent 1
## 32 East Flatbush 1
## 33 East German 1
## 34 East Germans 1
## 42 East haunts 1
## 35 East Hollywood 1
## 43 East in 1
## 36 East Jerusalem 1
## 37 East Poland 1
## 38 East River 1
## 39 East Room 1
## 40 East Timor 1
## 44 North African 1
## 45 North Americas 1
## 46 North Carolinas 1
## 47 North English 1
## 50 North gates 1
## 51 North to 1
## 48 North Vietnamese 1
## 49 North Water 1
## 58 South as 1
## 52 South Asia 1
## 53 South Asian 1
## 54 South Dakotas 1
## 55 South Florida 1
## 59 South in 1
## 60 South of 1
## 56 South Rhodesias 1
## 57 South Will 1
## 61 West 47th 1
## 62 West 50th 1
## 63 West Florrissant 1
## 67 West in 1
## 68 West is 1
## 64 West Rivers 1
## 65 West Sider 1
## 66 West Wing 1
## label step_major
## 3 extract.features_process.text 3
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed
## 3 0 47.282 57.807 10.525
## 4 1 57.808 NA NA
## [1] "Remaining compound terms in Headline: "
## [1] "Remaining compound terms in Snippet: "
## [1] "Remaining compound terms in Abstract: "
## label step_major
## 4 extract.features_process.text_reporting_compound_terms 3
## 5 extract.features_build.corpus 4
## step_minor bgn end elapsed
## 4 1 57.808 57.814 0.006
## 5 0 57.815 NA NA
## [1] "Building glb_corpus_lst..."
## label step_major step_minor bgn end
## 5 extract.features_build.corpus 4 0 57.815 71.077
## 6 extract.features_extract.DTM 5 0 71.078 NA
## elapsed
## 5 13.262
## 6 NA
## [1] "Extracting TfIDf terms for Headline..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0)## [1] "Extracting TfIDf terms for Snippet..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0)## [1] "Extracting TfIDf terms for Abstract..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0)## label step_major step_minor bgn end elapsed
## 6 extract.features_extract.DTM 5 0 71.078 85.363 14.286
## 7 extract.features_report.DTM 6 0 85.364 NA NA
## [1] "Reporting TfIDf terms for Headline..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 8402, terms: 9218)>>
## Non-/sparse entries: 44258/77405378
## Sparsity : 100%
## Maximal term length: 31
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 8402, terms: 28)>>
## Non-/sparse entries: 4546/230710
## Sparsity : 98%
## Maximal term length: 10
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).
## [1] "Reporting TfIDf terms for Snippet..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 8402, terms: 13693)>>
## Non-/sparse entries: 105021/114943565
## Sparsity : 100%
## Maximal term length: 25
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 8402, terms: 22)>>
## Non-/sparse entries: 8276/176568
## Sparsity : 96%
## Maximal term length: 10
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).
## [1] "Reporting TfIDf terms for Abstract..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 8402, terms: 13737)>>
## Non-/sparse entries: 105374/115312900
## Sparsity : 100%
## Maximal term length: 112
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 8402, terms: 22)>>
## Non-/sparse entries: 8288/176556
## Sparsity : 96%
## Maximal term length: 10
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).
## label step_major step_minor bgn end elapsed
## 7 extract.features_report.DTM 6 0 85.364 123.68 38.316
## 8 extract.features_bind.DTM 7 0 123.681 NA NA
## [1] "Binding DTM for Headline..."
## [1] "Binding DTM for Snippet..."
## [1] "Binding DTM for Abstract..."
## label step_major step_minor bgn end elapsed
## 8 extract.features_bind.DTM 7 0 123.681 123.769 0.088
## 9 extract.features_bind.DXM 8 0 123.770 NA NA## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 extract.features_bind.DXM 8 0 123.770 193.031 69.264
## 10 extract.features_end 9 0 193.035 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 7 extract.features_report.DTM 6
## 6 extract.features_extract.DTM 5
## 5 extract.features_build.corpus 4
## 3 extract.features_process.text 3
## 2 extract.features_factorize.str.vars 2
## 8 extract.features_bind.DTM 7
## 1 extract.features_bgn 1
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed duration
## 9 0 123.770 193.031 69.264 69.261
## 7 0 85.364 123.680 38.316 38.316
## 6 0 71.078 85.363 14.286 14.285
## 5 0 57.815 71.077 13.262 13.262
## 3 0 47.282 57.807 10.525 10.525
## 2 0 46.540 47.281 0.741 0.741
## 8 0 123.681 123.769 0.088 0.088
## 1 0 46.529 46.539 0.011 0.010
## 4 1 57.808 57.814 0.006 0.006
## [1] "Total Elapsed Time: 193.031 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 6 extract.features 3 0 46.478 195.058 148.58
## 7 cluster.data 4 0 195.058 NA NAStep 4.0: cluster data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stophere; sav_allobs_df <- glb_allobs_df;
print("Clustering features: ")
print(cluster_vars <- grep("[HSA]\\.[PT]\\.", names(glb_allobs_df), value=TRUE))
#print(cluster_vars <- grep("[HSA]\\.", names(glb_allobs_df), value=TRUE))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (myCategory in c("##", "Business#Business Day#Dealbook", "OpEd#Opinion#",
"Styles#U.S.#", "Business#Technology#", "Science#Health#",
"Culture#Arts#")) {
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df$myCategory == myCategory, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df$myCategory.fctr) / minClusterSize=20)
# which(levels(glb_allobs_df$myCategory.fctr) == myCategory)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df$myCategory==myCategory,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_allobs_df,
c("myCategory", ".clusterid", glb_rsp_var)))
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + .clusterid ~
Popular.fctr, sum, value.var=".n"))
print(ctgry_cast_df)
#print(orderBy(~ myCategory -Y -NA, ctgry_cast_df))
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_clst.csv"),
# row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df$.clusterid,
glb_allobs_df[, glb_rsp_var],
useNA="ifany"))
# dsp_obs(.clusterid=1, myCategory="OpEd#Opinion#",
# cols=c("UniqueID", "Popular", "myCategory", ".clusterid", "Headline"),
# all=TRUE)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features["myCategory.fctr"] <- c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}## Loading required package: proxy
##
## Attaching package: 'proxy'
##
## The following objects are masked from 'package:stats':
##
## as.dist, dist
##
## The following object is masked from 'package:base':
##
## as.matrix
##
## Loading required package: dynamicTreeCut## [1] "Clustering features: "
## [1] "H.T.X2014" "H.T.X2015"
## [3] "H.T.art" "H.T.bank"
## [5] "H.T.big" "H.T.billion"
## [7] "H.T.busi" "H.T.china"
## [9] "H.T.daili" "H.T.day"
## [11] "H.T.deal" "H.T.fashion"
## [13] "H.T.first" "H.T.make"
## [15] "H.T.morn" "H.T.new"
## [17] "H.T.news" "H.T.newyork"
## [19] "H.T.obama" "H.T.pictur"
## [21] "H.T.polit" "H.T.report"
## [23] "H.T.say" "H.T.springsumm"
## [25] "H.T.take" "H.T.test"
## [27] "H.T.today" "H.T.week"
## [29] "S.T.articl" "S.T.can"
## [31] "S.T.compani" "S.T.day"
## [33] "S.T.fashion" "S.T.first"
## [35] "S.T.intern" "S.T.make"
## [37] "S.T.new" "S.T.newyork"
## [39] "S.T.newyorktim" "S.T.one"
## [41] "S.T.presid" "S.T.report"
## [43] "S.T.said" "S.T.share"
## [45] "S.T.show" "S.T.take"
## [47] "S.T.time" "S.T.week"
## [49] "S.T.will" "S.T.year"
## [51] "A.T.articl" "A.T.can"
## [53] "A.T.compani" "A.T.day"
## [55] "A.T.fashion" "A.T.first"
## [57] "A.T.intern" "A.T.make"
## [59] "A.T.new" "A.T.newyork"
## [61] "A.T.newyorktim" "A.T.one"
## [63] "A.T.presid" "A.T.report"
## [65] "A.T.said" "A.T.share"
## [67] "A.T.show" "A.T.take"
## [69] "A.T.time" "A.T.week"
## [71] "A.T.will" "A.T.year"
## [73] "H.T.clip" "H.T.ebola"
## [75] "H.T.get" "H.T.newyorktim"
## [77] "H.T.read" "H.T.word"
## [79] "H.P.http" "H.P.year.colon"
## [81] "H.P.daily.clip.report" "H.P.fashion.week"
## [83] "H.P.first.draft" "H.P.facts.figures"
## [85] "H.P.friday.night.music" "H.P.no.comment.colon"
## [87] "H.P.on.this.day" "H.P.quandary"
## [89] "H.P.readers.respond" "H.P.recap.colon"
## [91] "H.P.s.notebook" "H.P.today.in.politic"
## [93] "H.P.today.in.smallbusiness" "H.P.verbatim.colon"
## [95] "H.P.what.we.are" "S.T.appear"
## [97] "S.T.archiv" "S.T.diari"
## [99] "S.T.editor" "S.T.herald"
## [101] "S.T.obama" "S.T.photo"
## [103] "S.T.senat" "S.T.tribun"
## [105] "S.T.word" "S.P.http"
## [107] "S.P.year.colon" "S.P.daily.clip.report"
## [109] "S.P.fashion.week" "S.P.first.draft"
## [111] "S.P.metropolitan.diary.colon" "A.T.appear"
## [113] "A.T.archiv" "A.T.diari"
## [115] "A.T.editor" "A.T.herald"
## [117] "A.T.obama" "A.T.photo"
## [119] "A.T.senat" "A.T.tribun"
## [121] "A.T.word" "A.P.http"
## [123] "A.P.year.colon" "A.P.daily.clip.report"
## [125] "A.P.fashion.week" "A.P.first.draft"
## [127] "A.P.metropolitan.diary.colon"
## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory
## 105 105 0 ##
## 95 95 1 ##
## Headline
## 105 When Buildings Are Political, Should Architects Be Politicians?
## 95 Phrases We Love Too Much
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 105 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 105 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 105 0 0 0 0 0 0 1.532407
## 95 0 0 0 0 0 0 0.000000
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 105 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 105 0 0 0 0 0 0
## 95 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 105 0 0.0000000 0 0 0 0
## 95 0 0.8094347 0 0 0 0
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 105 0 0 0 0 0 0.3554158 0
## 95 0 0 0 0 0 0.0000000 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 105 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 105 0 0 0 0.000000 0 0
## 95 0 0 0 0.808607 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 105 0 0 0 0 0 0
## 95 0 0 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 105 0 0.3550306 0 0 0 0 0
## 95 0 0.0000000 0 0 0 0 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 105 0 0 0 0 0 0
## 95 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 105 0 0 0 0
## 95 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 105 0 0 0
## 95 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 105 0 0 0 0
## 95 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 105 0 0 0
## 95 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 105 0 0 0 0 0
## 95 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun
## 105 0 0 0 0 0 0
## 95 0 0 0 0 0 0
## S.T.word S.P.http S.P.year.colon S.P.daily.clip.report
## 105 0 0 0 0
## 95 0 0 0 0
## S.P.fashion.week S.P.first.draft S.P.metropolitan.diary.colon
## 105 0 0 0
## 95 0 0 0
## A.T.appear A.T.archiv A.T.diari A.T.editor A.T.herald A.T.obama
## 105 0 0 0 0 0 0
## 95 0 0 0 0 0 0
## A.T.photo A.T.senat A.T.tribun A.T.word A.P.http A.P.year.colon
## 105 0 0 0 0 0 0
## 95 0 0 0 0 0 0
## A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 105 0 0 0
## 95 0 0 0
## A.P.metropolitan.diary.colon
## 105 0
## 95 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory
## 1490 1490 0 ##
## 2794 2794 0 ##
## Headline
## 1490 Top Republicans Send Warning -- and Appeal -- to Donors
## 2794 More Interesting Than What Kay Hagan Raised? What She Spent.
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 1490 0 0 0 0 0 0 0
## 2794 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 1490 0 0 0 0 0 0 0
## 2794 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 1490 0 0 0 0 0 0 0
## 2794 0 0 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today
## 1490 0 0 0 0 0 0
## 2794 0 0 0 0 0 0
## H.T.week S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 1490 0 0 0 0 0 0 0
## 2794 0 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 1490 0 0 0 0 0 0
## 2794 0 0 0 0 0 0
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 1490 0 0 0 0 0 0 0
## 2794 0 0 0 0 0 0 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 1490 0 0 0 0 0 0 0
## 2794 0 0 0 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 1490 0 0 0 0 0 0
## 2794 0 0 0 0 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 1490 0 0 0 0 0 0
## 2794 0 0 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 1490 0 0 0 0 0 0 0
## 2794 0 0 0 0 0 0 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 1490 0 0 0 0 0 0
## 2794 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 1490 0 0 0 0
## 2794 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 1490 0 0 0
## 2794 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 1490 0 0 0 0
## 2794 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 1490 0 0 0
## 2794 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 1490 0 0 0 0 0
## 2794 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun
## 1490 0 0 0 0 0.6397067 0
## 2794 0 0 0 0 0.4264712 0
## S.T.word S.P.http S.P.year.colon S.P.daily.clip.report
## 1490 0 0 0 0
## 2794 0 0 0 0
## S.P.fashion.week S.P.first.draft S.P.metropolitan.diary.colon
## 1490 0 0 0
## 2794 0 0 0
## A.T.appear A.T.archiv A.T.diari A.T.editor A.T.herald A.T.obama
## 1490 0 0 0 0 0 0
## 2794 0 0 0 0 0 0
## A.T.photo A.T.senat A.T.tribun A.T.word A.P.http A.P.year.colon
## 1490 0 0.6389631 0 0 0 0
## 2794 0 0.4259754 0 0 0 0
## A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 1490 0 0 0
## 2794 0 0 0
## A.P.metropolitan.diary.colon
## 1490 0
## 2794 0
## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory
## 29 29 0 Business#Business Day#Dealbook
## 27 27 0 Business#Business Day#Dealbook
## Headline H.T.X2014
## 29 Heineken to Sell Mexican Packaging Unit to Crown Holdings 0
## 27 Fund Manager Neil Woodford Sells HSBC Stake on Risk of Fines 0
## H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi H.T.china
## 29 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make H.T.morn
## 29 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit H.T.report
## 29 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week S.T.articl
## 29 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## S.T.can S.T.compani S.T.day S.T.fashion S.T.first S.T.intern
## 29 0.3104759 0 0 0 0 0
## 27 0.0000000 0 0 0 0 0
## S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one S.T.presid
## 29 0.3237739 0 0 0 0 0
## 27 0.0000000 0 0 0 0 0
## S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time S.T.week
## 29 0 0.0000000 0 0 0 0 0
## 27 0 0.3290046 0 0 0 0 0
## S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day A.T.fashion
## 29 0 0 0 0.3104759 0 0 0
## 27 0 0 0 0.0000000 0 0 0
## A.T.first A.T.intern A.T.make A.T.new A.T.newyork A.T.newyorktim
## 29 0 0 0.3234428 0 0 0
## 27 0 0 0.0000000 0 0 0
## A.T.one A.T.presid A.T.report A.T.said A.T.share A.T.show A.T.take
## 29 0 0 0 0.0000000 0 0 0
## 27 0 0 0 0.3290046 0 0 0
## A.T.time A.T.week A.T.will A.T.year H.T.clip H.T.ebola H.T.get
## 29 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## H.T.newyorktim H.T.read H.T.word H.P.http H.P.year.colon
## 29 0 0 0 0 0
## 27 0 0 0 0 0
## H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 29 0 0 0
## 27 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 29 0 0 0
## 27 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 29 0 0 0 0
## 27 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 29 0 0 0
## 27 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 29 0 0 0 0 0
## 27 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word
## 29 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## S.P.http S.P.year.colon S.P.daily.clip.report S.P.fashion.week
## 29 0 0 0 0
## 27 0 0 0 0
## S.P.first.draft S.P.metropolitan.diary.colon A.T.appear A.T.archiv
## 29 0 0 0 0
## 27 0 0 0 0
## A.T.diari A.T.editor A.T.herald A.T.obama A.T.photo A.T.senat
## 29 0 0 0 0 0 0
## 27 0 0 0 0 0 0
## A.T.tribun A.T.word A.P.http A.P.year.colon A.P.daily.clip.report
## 29 0 0 0 0 0
## 27 0 0 0 0 0
## A.P.fashion.week A.P.first.draft A.P.metropolitan.diary.colon
## 29 0 0 0
## 27 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory
## 486 486 0 Business#Business Day#Dealbook
## 666 666 0 Business#Business Day#Dealbook
## Headline H.T.X2014
## 486 Pension Funds Join Lawsuit on High-Frequency Trading 0
## 666 Coinbase Extends Bitcoin Access to International Customers 0
## H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi H.T.china
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make H.T.morn
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit H.T.report
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week S.T.articl
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## S.T.can S.T.compani S.T.day S.T.fashion S.T.first S.T.intern S.T.make
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## S.T.new S.T.newyork S.T.newyorktim S.T.one S.T.presid S.T.report
## 486 0 0 0 0.2477271 0 0
## 666 0 0 0 0.2346889 0 0
## S.T.said S.T.share S.T.show S.T.take S.T.time S.T.week S.T.will
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## S.T.year A.T.articl A.T.can A.T.compani A.T.day A.T.fashion A.T.first
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## A.T.intern A.T.make A.T.new A.T.newyork A.T.newyorktim A.T.one
## 486 0 0 0 0 0 0.2475176
## 666 0 0 0 0 0 0.2344903
## A.T.presid A.T.report A.T.said A.T.share A.T.show A.T.take A.T.time
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## A.T.week A.T.will A.T.year H.T.clip H.T.ebola H.T.get H.T.newyorktim
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## H.T.read H.T.word H.P.http H.P.year.colon H.P.daily.clip.report
## 486 0 0 0 0 0
## 666 0 0 0 0 0
## H.P.fashion.week H.P.first.draft H.P.facts.figures
## 486 0 0 0
## 666 0 0 0
## H.P.friday.night.music H.P.no.comment.colon H.P.on.this.day
## 486 0 0 0
## 666 0 0 0
## H.P.quandary H.P.readers.respond H.P.recap.colon H.P.s.notebook
## 486 0 0 0 0
## 666 0 0 0 0
## H.P.today.in.politic H.P.today.in.smallbusiness H.P.verbatim.colon
## 486 0 0 0
## 666 0 0 0
## H.P.what.we.are S.T.appear S.T.archiv S.T.diari S.T.editor S.T.herald
## 486 0 0 0 0 0 0
## 666 0 0 0 0 0 0
## S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 486 0 0 0 0 0 0
## 666 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 486 0 0 0 0
## 666 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari
## 486 0 0 0 0
## 666 0 0 0 0
## A.T.editor A.T.herald A.T.obama A.T.photo A.T.senat A.T.tribun
## 486 0 0 0 0 0 0
## 666 0 0 0 0 0 0
## A.T.word A.P.http A.P.year.colon A.P.daily.clip.report
## 486 0 0 0 0
## 666 0 0 0 0
## A.P.fashion.week A.P.first.draft A.P.metropolitan.diary.colon
## 486 0 0 0
## 666 0 0 0
## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory
## 20 20 0 OpEd#Opinion#
## 9 9 1 OpEd#Opinion#
## Headline
## 20 Is Rational Water Management Finally Emerging in Dry California?
## 9 Why New York City Opposed Abe Lincoln
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 20 0 0 0 0.0000000 0 0 0
## 9 0 0 0 0.9835152 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first S.T.intern
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one S.T.presid
## 20 0 0 0 0 0 0.0000000
## 9 0 0 0 0 0 0.3831438
## S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time S.T.week
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day A.T.fashion
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## A.T.first A.T.intern A.T.make A.T.new A.T.newyork A.T.newyorktim
## 20 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## A.T.one A.T.presid A.T.report A.T.said A.T.share A.T.show A.T.take
## 20 0 0.0000000 0 0 0 0 0
## 9 0 0.3831438 0 0 0 0 0
## A.T.time A.T.week A.T.will A.T.year H.T.clip H.T.ebola H.T.get
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## H.T.newyorktim H.T.read H.T.word H.P.http H.P.year.colon
## 20 0 0 0 0 0
## 9 0 0 0 0 0
## H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 20 0 0 0
## 9 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 20 0 0 0
## 9 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 20 0 0 0 0
## 9 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 20 0 0 0
## 9 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 20 0 0 0 0 0
## 9 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## S.P.http S.P.year.colon S.P.daily.clip.report S.P.fashion.week
## 20 0 0 0 0
## 9 0 0 0 0
## S.P.first.draft S.P.metropolitan.diary.colon A.T.appear A.T.archiv
## 20 0 0 0 0
## 9 0 0 0 0
## A.T.diari A.T.editor A.T.herald A.T.obama A.T.photo A.T.senat
## 20 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## A.T.tribun A.T.word A.P.http A.P.year.colon A.P.daily.clip.report
## 20 0 0 0 0 0
## 9 0 0 0 0 0
## A.P.fashion.week A.P.first.draft A.P.metropolitan.diary.colon
## 20 0 0 0
## 9 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory Headline
## 52 52 1 OpEd#Opinion# Steven Sotloff: Another ISIS Atrocity
## 36 36 1 OpEd#Opinion# Midterms on the Menu
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 52 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 52 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 52 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 52 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first S.T.intern
## 52 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one S.T.presid
## 52 0 0 0 0 0 0.3831438
## 36 0 0 0 0 0 0.3831438
## S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time S.T.week
## 52 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day A.T.fashion
## 52 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## A.T.first A.T.intern A.T.make A.T.new A.T.newyork A.T.newyorktim
## 52 0 0 0 0 0 0
## 36 0 0 0 0 0 0
## A.T.one A.T.presid A.T.report A.T.said A.T.share A.T.show A.T.take
## 52 0 0.3831438 0 0 0 0 0
## 36 0 0.3831438 0 0 0 0 0
## A.T.time A.T.week A.T.will A.T.year H.T.clip H.T.ebola H.T.get
## 52 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## H.T.newyorktim H.T.read H.T.word H.P.http H.P.year.colon
## 52 0 0 0 0 0
## 36 0 0 0 0 0
## H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 52 0 0 0
## 36 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 52 0 0 0
## 36 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 52 0 0 0 0
## 36 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 52 0 0 0
## 36 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 52 0 0 0 0 0
## 36 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word
## 52 0 0 0.4289812 0 0 0 0
## 36 0 0 0.4289812 0 0 0 0
## S.P.http S.P.year.colon S.P.daily.clip.report S.P.fashion.week
## 52 0 0 0 0
## 36 0 0 0 0
## S.P.first.draft S.P.metropolitan.diary.colon A.T.appear A.T.archiv
## 52 0 0 0 0
## 36 0 0 0 0
## A.T.diari A.T.editor A.T.herald A.T.obama A.T.photo A.T.senat
## 52 0 0 0 0.4294895 0 0
## 36 0 0 0 0.4294895 0 0
## A.T.tribun A.T.word A.P.http A.P.year.colon A.P.daily.clip.report
## 52 0 0 0 0 0
## 36 0 0 0 0 0
## A.P.fashion.week A.P.first.draft A.P.metropolitan.diary.colon
## 52 0 0 0
## 36 0 0 0
## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory
## 87 87 1 Styles#U.S.#
## 157 157 1 Styles#U.S.#
## Headline H.T.X2014
## 87 Homework and Consequences 0
## 157 Weekly Quandary: Should You Push a Child to Play a Sport? 0
## H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi H.T.china
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make H.T.morn
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit H.T.report
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 87 0 0 0 0 0 0.0000000
## 157 0 0 0 0 0 0.8266169
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 87 0 0 0 0 0 0
## 157 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 87 0 0.0000000 0 0 0 0
## 157 0 0.4415098 0 0 0 0
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 87 0 0 0 0.0000000 0 0
## 157 0 0 0 0.4410583 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 87 0 0 0 0 0 0
## 157 0 0 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 87 0 0 0 0 0 0
## 157 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 87 0 0 0 0
## 157 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 87 0 0 0
## 157 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 87 0 0 0 0
## 157 0 1 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 87 0 0 0
## 157 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 87 0 0 0 0 0
## 157 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun
## 87 0 0 0 0 0 0
## 157 0 0 0 0 0 0
## S.T.word S.P.http S.P.year.colon S.P.daily.clip.report
## 87 0 0 0 0
## 157 0 0 0 0
## S.P.fashion.week S.P.first.draft S.P.metropolitan.diary.colon
## 87 0 0 0
## 157 0 0 0
## A.T.appear A.T.archiv A.T.diari A.T.editor A.T.herald A.T.obama
## 87 0 0 0 0 0 0
## 157 0 0 0 0 0 0
## A.T.photo A.T.senat A.T.tribun A.T.word A.P.http A.P.year.colon
## 87 0 0 0 0 0 0
## 157 0 0 0 0 0 0
## A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 87 0 0 0
## 157 0 0 0
## A.P.metropolitan.diary.colon
## 87 0
## 157 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory
## 1477 1477 0 Styles#U.S.#
## 6409 6409 0 Styles#U.S.#
## Headline H.T.X2014 H.T.X2015
## 1477 From Lymphoma Mom to Just Mom, and Holding 0 0
## 6409 Intensive Thanksgiving 0 0
## H.T.art H.T.bank H.T.big H.T.billion H.T.busi H.T.china H.T.daili
## 1477 0 0 0 0 0 0 0
## 6409 0 0 0 0 0 0 0
## H.T.day H.T.deal H.T.fashion H.T.first H.T.make H.T.morn H.T.new
## 1477 0 0 0 0 0 0 0
## 6409 0 0 0 0 0 0 0
## H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit H.T.report
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 1477 0 0 0 0 0 0.3185063
## 6409 0 0 0 0 0 0.2346889
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 1477 0 0 0 0 0 0 0
## 6409 0 0 0 0 0 0 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 1477 0 0 0 0 0 0 0
## 6409 0 0 0 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 1477 0 0.3182369 0 0 0 0
## 6409 0 0.2344903 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 1477 0 0 0 0 0 0 0
## 6409 0 0 0 0 0 0 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 1477 0 0 0 0
## 6409 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 1477 0 0 0
## 6409 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 1477 0 0 0 0
## 6409 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 1477 0 0 0
## 6409 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 1477 0 0 0 0 0
## 6409 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## S.T.word S.P.http S.P.year.colon S.P.daily.clip.report
## 1477 0 0 0 0
## 6409 0 0 0 0
## S.P.fashion.week S.P.first.draft S.P.metropolitan.diary.colon
## 1477 0 0 0
## 6409 0 0 0
## A.T.appear A.T.archiv A.T.diari A.T.editor A.T.herald A.T.obama
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## A.T.photo A.T.senat A.T.tribun A.T.word A.P.http A.P.year.colon
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 1477 0 0 0
## 6409 0 0 0
## A.P.metropolitan.diary.colon
## 1477 0
## 6409 0
## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory
## 26 26 0 Business#Technology#
## 104 104 1 Business#Technology#
## Headline
## 26 Daily Report: Start-Ups Stockpile Funding for Harder Times to Come
## 104 German Court Bans Uber Service Nationwide
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 26 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 26 0 0.6602037 0 0 0 0 0
## 104 0 0.0000000 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 26 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 26 0.6166537 0 0 0 0 0 0
## 104 0.0000000 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 26 0 0 0 0 0 0
## 104 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 26 0 0 0 0 0 0
## 104 0 0 0 0 0 0
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 26 0 0 0.000000 0 0 0.3317214 0.3244395
## 104 0 0 0.270945 0 0 0.0000000 0.0000000
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 26 0 0.0000000 0.0000000 0 0 0 0
## 104 0 0.2067598 0.2259859 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 26 0 0 0 0 0 0
## 104 0 0 0 0 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 26 0 0 0 0 0.000000 0
## 104 0 0 0 0 0.270945 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 26 0 0.3313619 0.3241061 0 0.0000000 0.0000000 0
## 104 0 0.0000000 0.0000000 0 0.2065292 0.2259859 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 26 0 0 0 0 0 0
## 104 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 26 0 0 0 0
## 104 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 26 0 0 0
## 104 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 26 0 0 0 0
## 104 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 26 0 0 0
## 104 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 26 0 0 0 0 0
## 104 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun
## 26 0 0 0 0 0 0
## 104 0 0 0 0 0 0
## S.T.word S.P.http S.P.year.colon S.P.daily.clip.report
## 26 0 0 0 0
## 104 0 0 0 0
## S.P.fashion.week S.P.first.draft S.P.metropolitan.diary.colon
## 26 0 0 0
## 104 0 0 0
## A.T.appear A.T.archiv A.T.diari A.T.editor A.T.herald A.T.obama
## 26 0 0 0 0 0 0
## 104 0 0 0 0 0 0
## A.T.photo A.T.senat A.T.tribun A.T.word A.P.http A.P.year.colon
## 26 0 0 0 0 0 0
## 104 0 0 0 0 0 0
## A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 26 0 0 0
## 104 0 0 0
## A.P.metropolitan.diary.colon
## 26 0
## 104 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory
## 284 284 0 Business#Technology#
## 3397 3397 0 Business#Technology#
## Headline H.T.X2014 H.T.X2015
## 284 Cisco Has a Server-Centric Vision for Itself 0 0
## 3397 Reddit Debuts an Official Mobile App 0 0
## H.T.art H.T.bank H.T.big H.T.billion H.T.busi H.T.china H.T.daili
## 284 0 0 0 0 0 0 0
## 3397 0 0 0 0 0 0 0
## H.T.day H.T.deal H.T.fashion H.T.first H.T.make H.T.morn H.T.new
## 284 0 0 0 0 0 0 0
## 3397 0 0 0 0 0 0 0
## H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit H.T.report
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 284 0 0 0 0 0 0 0
## 3397 0 0 0 0 0 0 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 284 0 0 0.2561173 0 0 0 0
## 3397 0 0 0.2955200 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 284 0 0 0 0 0 0.2561173 0
## 3397 0 0 0 0 0 0.2955200 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
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## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
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## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
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## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
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## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 284 0 0 0 0 0
## 3397 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## S.T.word S.P.http S.P.year.colon S.P.daily.clip.report
## 284 0 0 0 0
## 3397 0 0 0 0
## S.P.fashion.week S.P.first.draft S.P.metropolitan.diary.colon
## 284 0 0 0
## 3397 0 0 0
## A.T.appear A.T.archiv A.T.diari A.T.editor A.T.herald A.T.obama
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## A.T.photo A.T.senat A.T.tribun A.T.word A.P.http A.P.year.colon
## 284 0 0 0 0 0 0
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## A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 284 0 0 0
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## A.P.metropolitan.diary.colon
## 284 0
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## [1] "max distance(1.0000) pair:"
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## 33 33 1 Science#Health# Don't Catch What Ails Your House
## 7 7 0 Science#Health# Birth Weight and Diabetes
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 33 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 33 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 33 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 33 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first S.T.intern
## 33 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one S.T.presid
## 33 0 0.0000000 0 0 0 0
## 7 0 0.2514869 0 0 0 0
## S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time S.T.week
## 33 0 0 0 0 0.3109888 0 0
## 7 0 0 0 0 0.0000000 0 0
## S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day A.T.fashion
## 33 0.2196823 0 0 0 0 0 0
## 7 0.0000000 0 0 0 0 0 0
## A.T.first A.T.intern A.T.make A.T.new A.T.newyork A.T.newyorktim
## 33 0 0 0 0.0000000 0 0
## 7 0 0 0 0.2513463 0 0
## A.T.one A.T.presid A.T.report A.T.said A.T.share A.T.show A.T.take
## 33 0 0 0 0 0 0 0.3106517
## 7 0 0 0 0 0 0 0.0000000
## A.T.time A.T.week A.T.will A.T.year H.T.clip H.T.ebola H.T.get
## 33 0 0 0.2194373 0 0 0 0
## 7 0 0 0.0000000 0 0 0 0
## H.T.newyorktim H.T.read H.T.word H.P.http H.P.year.colon
## 33 0 0 0 0 0
## 7 0 0 0 0 0
## H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 33 0 0 0
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## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 33 0 0 0
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## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 33 0 0 0 0
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## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 33 0 0 0
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## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 33 0 0 0 0 0
## 7 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word
## 33 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## S.P.http S.P.year.colon S.P.daily.clip.report S.P.fashion.week
## 33 0 0 0 0
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## S.P.first.draft S.P.metropolitan.diary.colon A.T.appear A.T.archiv
## 33 0 0 0 0
## 7 0 0 0 0
## A.T.diari A.T.editor A.T.herald A.T.obama A.T.photo A.T.senat
## 33 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## A.T.tribun A.T.word A.P.http A.P.year.colon A.P.daily.clip.report
## 33 0 0 0 0 0
## 7 0 0 0 0 0
## A.P.fashion.week A.P.first.draft A.P.metropolitan.diary.colon
## 33 0 0 0
## 7 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory Headline
## 1927 1927 1 Science#Health# My Father's A.L.S.
## 4619 4619 1 Science#Health# Pronouncing the Patient Dead
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today
## 1927 0 0 0 0 0 0
## 4619 0 0 0 0 0 0
## H.T.week S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 1927 0 0 0 0 0 0.3185063
## 4619 0 0 0 0 0 0.2346889
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 1927 0 0 0 0 0 0
## 4619 0 0 0 0 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 1927 0 0.3182369 0 0 0 0
## 4619 0 0.2344903 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 1927 0 0 0 0 0 0
## 4619 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 1927 0 0 0 0
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## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 1927 0 0 0
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## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 1927 0 0 0 0
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## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
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## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 1927 0 0 0 0 0
## 4619 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun
## 1927 0 0 0 0 0 0
## 4619 0 0 0 0 0 0
## S.T.word S.P.http S.P.year.colon S.P.daily.clip.report
## 1927 0 0 0 0
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## S.P.fashion.week S.P.first.draft S.P.metropolitan.diary.colon
## 1927 0 0 0
## 4619 0 0 0
## A.T.appear A.T.archiv A.T.diari A.T.editor A.T.herald A.T.obama
## 1927 0 0 0 0 0 0
## 4619 0 0 0 0 0 0
## A.T.photo A.T.senat A.T.tribun A.T.word A.P.http A.P.year.colon
## 1927 0 0 0 0 0 0
## 4619 0 0 0 0 0 0
## A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 1927 0 0 0
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## A.P.metropolitan.diary.colon
## 1927 0
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## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory Headline
## 30 30 0 Culture#Arts# Fabio Luisi Has a New Gig
## 13 13 0 Culture#Arts# Kate Bush Bombards British Album Chart
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 30 0 1.201925 0 0 0 0 0
## 13 0 0.000000 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first S.T.intern
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one S.T.presid
## 30 0 0 0 0 0 0
## 13 0 0 0 0 0 0
## S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time S.T.week
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day A.T.fashion
## 30 0.2196823 0 0 0 0 0 0
## 13 0.0000000 0 0 0 0 0 0
## A.T.first A.T.intern A.T.make A.T.new A.T.newyork A.T.newyorktim
## 30 0 0 0 0 0 0
## 13 0 0 0 0 0 0
## A.T.one A.T.presid A.T.report A.T.said A.T.share A.T.show A.T.take
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## A.T.time A.T.week A.T.will A.T.year H.T.clip H.T.ebola H.T.get
## 30 0 0 0.2194373 0 0 0 0
## 13 0 0 0.0000000 0 0 0 0
## H.T.newyorktim H.T.read H.T.word H.P.http H.P.year.colon
## 30 0 0 0 0 0
## 13 0 0 0 0 0
## H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 30 0 0 0
## 13 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 30 0 0 0
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## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 30 0 0 0 0
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## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 30 0 0 0
## 13 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 30 0 0 0 0 0
## 13 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## S.P.http S.P.year.colon S.P.daily.clip.report S.P.fashion.week
## 30 0 0 0 0
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## S.P.first.draft S.P.metropolitan.diary.colon A.T.appear A.T.archiv
## 30 0 0 0 0
## 13 0 0 0 0
## A.T.diari A.T.editor A.T.herald A.T.obama A.T.photo A.T.senat
## 30 0 0 0 0 0 0
## 13 0 0 0 0 0 0
## A.T.tribun A.T.word A.P.http A.P.year.colon A.P.daily.clip.report
## 30 0 0 0 0 0
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## A.P.fashion.week A.P.first.draft A.P.metropolitan.diary.colon
## 30 0 0 0
## 13 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory
## 10 10 0 Culture#Arts#
## 93 93 0 Culture#Arts#
## Headline
## 10 Lars von Trier Returning to TV
## 93 'Lord of the Rings' Trilogy, With Live Orchestra, Coming to Lincoln Center
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 10 0 0 0 0 0 0 0
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## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 10 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 10 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 10 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first S.T.intern
## 10 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0
## S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one S.T.presid
## 10 0 0 0 0 0 0
## 93 0 0 0 0 0 0
## S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time S.T.week
## 10 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0
## S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day A.T.fashion
## 10 0.2929097 0 0 0 0 0 0
## 93 0.2510655 0 0 0 0 0 0
## A.T.first A.T.intern A.T.make A.T.new A.T.newyork A.T.newyorktim
## 10 0 0 0 0 0 0
## 93 0 0 0 0 0 0
## A.T.one A.T.presid A.T.report A.T.said A.T.share A.T.show A.T.take
## 10 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0
## A.T.time A.T.week A.T.will A.T.year H.T.clip H.T.ebola H.T.get
## 10 0 0 0.2925830 0 0 0 0
## 93 0 0 0.2507854 0 0 0 0
## H.T.newyorktim H.T.read H.T.word H.P.http H.P.year.colon
## 10 0 0 0 0 0
## 93 0 0 0 0 0
## H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 10 0 0 0
## 93 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 10 0 0 0
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## 10 0 0 0 0
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## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 10 0 0 0
## 93 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 10 0 0 0 0 0
## 93 0 0 0 0 0
## S.T.editor S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word
## 10 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0
## S.P.http S.P.year.colon S.P.daily.clip.report S.P.fashion.week
## 10 0 0 0 0
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## S.P.first.draft S.P.metropolitan.diary.colon A.T.appear A.T.archiv
## 10 0 0 0 0
## 93 0 0 0 0
## A.T.diari A.T.editor A.T.herald A.T.obama A.T.photo A.T.senat
## 10 0 0 0 0 0 0
## 93 0 0 0 0 0 0
## A.T.tribun A.T.word A.P.http A.P.year.colon A.P.daily.clip.report
## 10 0 0 0 0 0
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## A.P.fashion.week A.P.first.draft A.P.metropolitan.diary.colon
## 10 0 0 0
## 93 0 0 0
## myCategory .clusterid N Y NA
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## 39 Business#Crosswords/Games# 1 20 103 42
## 75 Science#Health# 1 28 66 37
## 80 Styles#U.S.# 1 28 46 34
## 1 ## 1 193 38 43
## 68 OpEd#Opinion# 2 16 36 22
## 69 OpEd#Opinion# 3 19 36 14
## 50 Culture#Arts# 4 31 33 8
## 70 OpEd#Opinion# 4 8 28 11
## 22 Business#Business Day#Dealbook 1 239 27 63
## 81 Styles#U.S.# 2 23 27 14
## 71 OpEd#Opinion# 5 8 21 4
## 78 Science#Health# 4 16 20 3
## 82 Styles#U.S.# 3 13 19 11
## 77 Science#Health# 3 15 19 7
## 72 OpEd#Opinion# 6 6 19 5
## 65 Metro#N.Y. / Region# 1 181 17 67
## 76 Science#Health# 2 15 17 10
## 3 ## 3 96 16 35
## 20 #Opinion#The Public Editor 1 4 16 10
## 6 ## 6 88 14 14
## 8 ## 8 59 14 12
## 73 OpEd#Opinion# 7 2 14 8
## 40 Business#Technology# 1 65 12 30
## 43 Business#Technology# 4 36 12 14
## 42 Business#Technology# 3 49 11 19
## 85 TStyle## 1 715 9 105
## 23 Business#Business Day#Dealbook 2 66 9 30
## 74 OpEd#Opinion# 8 7 8 6
## 83 Styles#U.S.# 4 13 8 2
## 24 Business#Business Day#Dealbook 3 60 7 34
## 26 Business#Business Day#Dealbook 5 50 7 22
## 31 Business#Business Day#Dealbook 10 34 7 12
## 47 Culture#Arts# 1 144 6 71
## 7 ## 7 70 6 27
## 41 Business#Technology# 2 62 6 25
## 46 Business#Technology# 7 23 6 8
## 38 Business#Business Day#Small Business 1 135 5 41
## 28 Business#Business Day#Dealbook 7 47 5 19
## 29 Business#Business Day#Dealbook 8 40 5 15
## 13 ## 13 30 5 13
## 11 ## 11 50 5 5
## 25 Business#Business Day#Dealbook 4 59 4 16
## 34 Business#Business Day#Dealbook 13 28 4 13
## 44 Business#Technology# 5 24 4 10
## 64 Foreign#World#Asia Pacific 1 200 3 56
## 2 ## 2 138 3 48
## 5 ## 5 84 3 43
## 48 Culture#Arts# 2 55 3 26
## 27 Business#Business Day#Dealbook 6 54 3 15
## 36 Business#Business Day#Dealbook 15 20 3 14
## 33 Business#Business Day#Dealbook 12 32 3 12
## 15 ## 15 29 3 6
## 18 #Multimedia# 1 139 2 52
## 51 Culture#Arts# 5 50 2 17
## 14 ## 14 32 2 13
## 32 Business#Business Day#Dealbook 11 35 2 12
## 57 Culture#Arts# 11 27 2 8
## 17 ## 17 17 2 5
## 84 Travel#Travel# 1 116 1 35
## 4 ## 4 113 1 27
## 19 #Opinion#Room For Debate 1 61 1 20
## 53 Culture#Arts# 7 25 1 18
## 58 Culture#Arts# 12 19 1 16
## 79 Styles##Fashion 1 118 1 15
## 10 ## 10 50 1 14
## 12 ## 12 34 1 14
## 30 Business#Business Day#Dealbook 9 39 1 14
## 16 ## 16 21 1 9
## 37 Business#Business Day#Dealbook 16 26 1 8
## 60 Culture#Arts# 14 19 1 7
## 62 Culture#Arts# 16 16 1 3
## 21 #U.S.#Education 1 325 0 89
## 63 Foreign#World# 1 172 0 47
## 52 Culture#Arts# 6 47 0 20
## 9 ## 9 65 0 14
## 49 Culture#Arts# 3 61 0 13
## 55 Culture#Arts# 9 29 0 11
## 59 Culture#Arts# 13 18 0 11
## 56 Culture#Arts# 10 30 0 9
## 45 Business#Technology# 6 29 0 8
## 35 Business#Business Day#Dealbook 14 35 0 5
## 66 myOther 1 38 0 5
## 54 Culture#Arts# 8 37 0 3
## 61 Culture#Arts# 15 18 0 3
## , , = N
##
##
## 1 2 3 4 5 6 7 8 9
## ## 193 138 96 113 84 88 70 59 65
## #Multimedia# 139 0 0 0 0 0 0 0 0
## #Opinion#Room For Debate 61 0 0 0 0 0 0 0 0
## #Opinion#The Public Editor 4 0 0 0 0 0 0 0 0
## #U.S.#Education 325 0 0 0 0 0 0 0 0
## Business#Business Day#Dealbook 239 66 60 59 50 54 47 40 39
## Business#Business Day#Small Business 135 0 0 0 0 0 0 0 0
## Business#Crosswords/Games# 20 0 0 0 0 0 0 0 0
## Business#Technology# 65 62 49 36 24 29 23 0 0
## Culture#Arts# 144 55 61 31 50 47 25 37 29
## Foreign#World# 172 0 0 0 0 0 0 0 0
## Foreign#World#Asia Pacific 200 0 0 0 0 0 0 0 0
## Metro#N.Y. / Region# 181 0 0 0 0 0 0 0 0
## myOther 38 0 0 0 0 0 0 0 0
## OpEd#Opinion# 51 16 19 8 8 6 2 7 0
## Science#Health# 28 15 15 16 0 0 0 0 0
## Styles##Fashion 118 0 0 0 0 0 0 0 0
## Styles#U.S.# 28 23 13 13 0 0 0 0 0
## Travel#Travel# 116 0 0 0 0 0 0 0 0
## TStyle## 715 0 0 0 0 0 0 0 0
##
## 10 11 12 13 14 15 16 17
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## #Opinion#The Public Editor 0 0 0 0 0 0 0 0
## #U.S.#Education 0 0 0 0 0 0 0 0
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## Business#Business Day#Small Business 0 0 0 0 0 0 0 0
## Business#Crosswords/Games# 0 0 0 0 0 0 0 0
## Business#Technology# 0 0 0 0 0 0 0 0
## Culture#Arts# 30 27 19 18 19 18 16 0
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## Foreign#World#Asia Pacific 0 0 0 0 0 0 0 0
## Metro#N.Y. / Region# 0 0 0 0 0 0 0 0
## myOther 0 0 0 0 0 0 0 0
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## , , = Y
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## Foreign#World# 0 0 0 0 0 0 0 0 0
## Foreign#World#Asia Pacific 3 0 0 0 0 0 0 0 0
## Metro#N.Y. / Region# 17 0 0 0 0 0 0 0 0
## myOther 0 0 0 0 0 0 0 0 0
## OpEd#Opinion# 247 36 36 28 21 19 14 8 0
## Science#Health# 66 17 19 20 0 0 0 0 0
## Styles##Fashion 1 0 0 0 0 0 0 0 0
## Styles#U.S.# 46 27 19 8 0 0 0 0 0
## Travel#Travel# 1 0 0 0 0 0 0 0 0
## TStyle## 9 0 0 0 0 0 0 0 0
##
## 10 11 12 13 14 15 16 17
## ## 1 5 1 5 2 3 1 2
## #Multimedia# 0 0 0 0 0 0 0 0
## #Opinion#Room For Debate 0 0 0 0 0 0 0 0
## #Opinion#The Public Editor 0 0 0 0 0 0 0 0
## #U.S.#Education 0 0 0 0 0 0 0 0
## Business#Business Day#Dealbook 7 2 3 4 0 3 1 0
## Business#Business Day#Small Business 0 0 0 0 0 0 0 0
## Business#Crosswords/Games# 0 0 0 0 0 0 0 0
## Business#Technology# 0 0 0 0 0 0 0 0
## Culture#Arts# 0 2 1 0 1 0 1 0
## Foreign#World# 0 0 0 0 0 0 0 0
## Foreign#World#Asia Pacific 0 0 0 0 0 0 0 0
## Metro#N.Y. / Region# 0 0 0 0 0 0 0 0
## myOther 0 0 0 0 0 0 0 0
## OpEd#Opinion# 0 0 0 0 0 0 0 0
## Science#Health# 0 0 0 0 0 0 0 0
## Styles##Fashion 0 0 0 0 0 0 0 0
## Styles#U.S.# 0 0 0 0 0 0 0 0
## Travel#Travel# 0 0 0 0 0 0 0 0
## TStyle## 0 0 0 0 0 0 0 0
##
## , , = NA
##
##
## 1 2 3 4 5 6 7 8 9
## ## 43 48 35 27 43 14 27 12 14
## #Multimedia# 52 0 0 0 0 0 0 0 0
## #Opinion#Room For Debate 20 0 0 0 0 0 0 0 0
## #Opinion#The Public Editor 10 0 0 0 0 0 0 0 0
## #U.S.#Education 89 0 0 0 0 0 0 0 0
## Business#Business Day#Dealbook 63 30 34 16 22 15 19 15 14
## Business#Business Day#Small Business 41 0 0 0 0 0 0 0 0
## Business#Crosswords/Games# 42 0 0 0 0 0 0 0 0
## Business#Technology# 30 25 19 14 10 8 8 0 0
## Culture#Arts# 71 26 13 8 17 20 18 3 11
## Foreign#World# 47 0 0 0 0 0 0 0 0
## Foreign#World#Asia Pacific 56 0 0 0 0 0 0 0 0
## Metro#N.Y. / Region# 67 0 0 0 0 0 0 0 0
## myOther 5 0 0 0 0 0 0 0 0
## OpEd#Opinion# 94 22 14 11 4 5 8 6 0
## Science#Health# 37 10 7 3 0 0 0 0 0
## Styles##Fashion 15 0 0 0 0 0 0 0 0
## Styles#U.S.# 34 14 11 2 0 0 0 0 0
## Travel#Travel# 35 0 0 0 0 0 0 0 0
## TStyle## 105 0 0 0 0 0 0 0 0
##
## 10 11 12 13 14 15 16 17
## ## 14 5 14 13 13 6 9 5
## #Multimedia# 0 0 0 0 0 0 0 0
## #Opinion#Room For Debate 0 0 0 0 0 0 0 0
## #Opinion#The Public Editor 0 0 0 0 0 0 0 0
## #U.S.#Education 0 0 0 0 0 0 0 0
## Business#Business Day#Dealbook 12 12 12 13 5 14 8 0
## Business#Business Day#Small Business 0 0 0 0 0 0 0 0
## Business#Crosswords/Games# 0 0 0 0 0 0 0 0
## Business#Technology# 0 0 0 0 0 0 0 0
## Culture#Arts# 9 8 16 11 7 3 3 0
## Foreign#World# 0 0 0 0 0 0 0 0
## Foreign#World#Asia Pacific 0 0 0 0 0 0 0 0
## Metro#N.Y. / Region# 0 0 0 0 0 0 0 0
## myOther 0 0 0 0 0 0 0 0
## OpEd#Opinion# 0 0 0 0 0 0 0 0
## Science#Health# 0 0 0 0 0 0 0 0
## Styles##Fashion 0 0 0 0 0 0 0 0
## Styles#U.S.# 0 0 0 0 0 0 0 0
## Travel#Travel# 0 0 0 0 0 0 0 0
## TStyle## 0 0 0 0 0 0 0 0# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 cluster.data 4 0 195.058 210.115 15.057
## 8 select.features 5 0 210.115 NA NAStep 5.0: select features
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## Popular Popular 1.000000e+00
## A.nuppr.log A.nuppr.log -2.685472e-01
## S.nuppr.log S.nuppr.log -2.682841e-01
## WordCount.log WordCount.log 2.675605e-01
## A.ratio.sum.TfIdf.nwrds A.ratio.sum.TfIdf.nwrds 2.653142e-01
## WordCount.log.nonNA WordCount.log.nonNA 2.652413e-01
## S.ratio.sum.TfIdf.nwrds S.ratio.sum.TfIdf.nwrds 2.651816e-01
## WordCount WordCount 2.575265e-01
## S.nwrds.unq.log S.nwrds.unq.log -2.373282e-01
## A.nwrds.unq.log A.nwrds.unq.log -2.372695e-01
## H.ratio.sum.TfIdf.nwrds H.ratio.sum.TfIdf.nwrds 2.254479e-01
## A.nchrs.log A.nchrs.log -2.054874e-01
## S.nchrs.log S.nchrs.log -2.054030e-01
## H.nwrds.unq.log H.nwrds.unq.log -2.013666e-01
## A.nwrds.log A.nwrds.log -1.895162e-01
## S.nwrds.log S.nwrds.log -1.893755e-01
## H.nchrs.log H.nchrs.log -1.710624e-01
## myCategory.fctr myCategory.fctr 1.703175e-01
## myCategory.fctr.nonNA myCategory.fctr.nonNA 1.703175e-01
## H.nwrds.log H.nwrds.log -1.573431e-01
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.1 1.567535e-01
## H.sum.TfIdf H.sum.TfIdf 1.519219e-01
## S.sum.TfIdf S.sum.TfIdf 1.469053e-01
## A.sum.TfIdf A.sum.TfIdf 1.462756e-01
## PubDate.hour.fctr PubDate.hour.fctr 1.354368e-01
## PubDate.hour.fctr.nonNA PubDate.hour.fctr.nonNA 1.354368e-01
## H.npnct19.log H.npnct19.log 1.283641e-01
## H.nuppr.log H.nuppr.log -1.278085e-01
## A.ndgts.log A.ndgts.log -1.246678e-01
## S.ndgts.log S.ndgts.log -1.239225e-01
## H.ndgts.log H.ndgts.log -1.196633e-01
## A.nstopwrds.log A.nstopwrds.log -1.144762e-01
## S.nstopwrds.log S.nstopwrds.log -1.138992e-01
## A.ratio.nstopwrds.nwrds A.ratio.nstopwrds.nwrds 1.112093e-01
## S.ratio.nstopwrds.nwrds S.ratio.nstopwrds.nwrds 1.105792e-01
## PubDate.wkend PubDate.wkend 1.067288e-01
## A.npnct11.log A.npnct11.log -9.183870e-02
## S.npnct11.log S.npnct11.log -9.158156e-02
## H.P.recap.colon H.P.recap.colon 9.008096e-02
## H.npnct28.log H.npnct28.log -8.917338e-02
## H.P.quandary H.P.quandary 8.734922e-02
## H.nstopwrds.log H.nstopwrds.log -8.657067e-02
## .clusterid .clusterid -8.644104e-02
## .clusterid.fctr .clusterid.fctr -8.644104e-02
## S.T.week S.T.week -8.552704e-02
## A.T.week A.T.week -8.542792e-02
## S.T.fashion S.T.fashion -8.417159e-02
## A.T.fashion A.T.fashion -8.416793e-02
## H.npnct15.log H.npnct15.log -8.273237e-02
## H.T.fashion H.T.fashion -7.946462e-02
## H.P.year.colon H.P.year.colon -7.842875e-02
## H.P.fashion.week H.P.fashion.week -7.632046e-02
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.4 7.394139e-02
## S.T.archiv S.T.archiv -7.202808e-02
## A.T.archiv A.T.archiv -7.202808e-02
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.2 7.097772e-02
## S.P.fashion.week S.P.fashion.week -7.080716e-02
## A.P.fashion.week A.P.fashion.week -7.080716e-02
## S.T.tribun S.T.tribun -7.013418e-02
## A.T.tribun A.T.tribun -7.013418e-02
## S.T.intern S.T.intern -6.956906e-02
## A.T.intern A.T.intern -6.953025e-02
## S.T.photo S.T.photo -6.874283e-02
## A.T.photo A.T.photo -6.873838e-02
## H.T.week H.T.week -6.827601e-02
## S.T.herald S.T.herald -6.752419e-02
## A.T.herald A.T.herald -6.752419e-02
## A.npnct15.log A.npnct15.log -6.735974e-02
## S.npnct15.log S.npnct15.log -6.612020e-02
## H.T.X2015 H.T.X2015 -6.601141e-02
## H.T.daili H.T.daili -6.303731e-02
## S.npnct04.log S.npnct04.log -6.294642e-02
## A.npnct04.log A.npnct04.log -6.294642e-02
## H.T.report H.T.report -6.238114e-02
## S.T.diari S.T.diari -6.229931e-02
## A.T.diari A.T.diari -6.229931e-02
## H.npnct14.log H.npnct14.log -6.158577e-02
## H.P.no.comment.colon H.P.no.comment.colon 6.074669e-02
## H.T.day H.T.day -6.044381e-02
## H.T.springsumm H.T.springsumm -5.943248e-02
## H.T.today H.T.today -5.833786e-02
## PubDate.day.minutes.poly.5 PubDate.day.minutes.poly.5 -5.592923e-02
## H.T.newyork H.T.newyork -5.563086e-02
## S.npnct19.log S.npnct19.log 5.552543e-02
## A.npnct19.log A.npnct19.log 5.531065e-02
## S.T.articl S.T.articl -5.471737e-02
## A.T.articl A.T.articl -5.470831e-02
## H.P.facts.figures H.P.facts.figures 5.410097e-02
## PubDate.last10 PubDate.last10 5.398093e-02
## H.npnct08.log H.npnct08.log 5.375262e-02
## S.npnct13.log S.npnct13.log -5.332519e-02
## H.npnct04.log H.npnct04.log -5.126277e-02
## S.T.share S.T.share -5.105597e-02
## A.T.share A.T.share -5.105597e-02
## A.npnct13.log A.npnct13.log -4.999563e-02
## S.T.newyorktim S.T.newyorktim -4.985328e-02
## A.T.newyorktim A.T.newyorktim -4.984782e-02
## PubDate.last10.log PubDate.last10.log 4.931702e-02
## H.T.busi H.T.busi -4.899819e-02
## S.T.editor S.T.editor -4.875454e-02
## A.T.editor A.T.editor -4.875454e-02
## H.T.morn H.T.morn -4.838380e-02
## S.T.word S.T.word -4.822452e-02
## A.T.word A.T.word -4.821561e-02
## S.T.compani S.T.compani -4.787994e-02
## S.T.report S.T.report -4.778378e-02
## A.T.compani A.T.compani -4.774812e-02
## A.T.report A.T.report -4.773092e-02
## S.T.newyork S.T.newyork -4.694998e-02
## A.T.newyork A.T.newyork -4.686921e-02
## PubDate.last1.log PubDate.last1.log 4.635751e-02
## H.T.X2014 H.T.X2014 -4.522109e-02
## H.T.first H.T.first -4.472902e-02
## H.T.news H.T.news -4.436368e-02
## H.P.readers.respond H.P.readers.respond 4.432886e-02
## H.T.clip H.T.clip -4.388279e-02
## H.P.daily.clip.report H.P.daily.clip.report -4.388279e-02
## S.P.daily.clip.report S.P.daily.clip.report -4.388279e-02
## A.P.daily.clip.report A.P.daily.clip.report -4.388279e-02
## A.npnct28.log A.npnct28.log -4.373349e-02
## S.npnct28.log S.npnct28.log -4.370037e-02
## H.P.first.draft H.P.first.draft -4.316253e-02
## S.T.first S.T.first -4.302271e-02
## A.T.first A.T.first -4.288090e-02
## H.P.today.in.smallbusiness H.P.today.in.smallbusiness -4.243051e-02
## A.T.day A.T.day -4.234061e-02
## S.T.day S.T.day -4.225390e-02
## A.T.show A.T.show -4.185292e-02
## S.T.show S.T.show -4.182920e-02
## S.T.senat S.T.senat -4.143422e-02
## A.T.senat A.T.senat -4.139980e-02
## A.T.make A.T.make 4.124187e-02
## S.T.make S.T.make 4.118050e-02
## H.T.new H.T.new -4.111696e-02
## H.ratio.nstopwrds.nwrds H.ratio.nstopwrds.nwrds 4.024406e-02
## H.T.pictur H.T.pictur -4.003882e-02
## PubDate.last100 PubDate.last100 3.989229e-02
## PubDate.wkday.fctr PubDate.wkday.fctr -3.980129e-02
## PubDate.wkday.fctr.nonNA PubDate.wkday.fctr.nonNA -3.980129e-02
## S.T.appear S.T.appear -3.941362e-02
## A.T.appear A.T.appear -3.941362e-02
## S.T.will S.T.will -3.850299e-02
## A.T.will A.T.will -3.845737e-02
## H.P.what.we.are H.P.what.we.are -3.775209e-02
## A.npnct12.log A.npnct12.log -3.760012e-02
## S.T.year S.T.year -3.756011e-02
## A.T.year A.T.year -3.741571e-02
## H.P.today.in.politic H.P.today.in.politic -3.733661e-02
## S.npnct12.log S.npnct12.log -3.638891e-02
## PubDate.last1 PubDate.last1 3.592267e-02
## H.T.read H.T.read -3.467043e-02
## PubDate.minute.fctr PubDate.minute.fctr -3.407385e-02
## PubDate.minute.fctr.nonNA PubDate.minute.fctr.nonNA -3.407385e-02
## H.T.get H.T.get 3.300192e-02
## H.T.art H.T.art -3.291486e-02
## H.T.china H.T.china -3.283653e-02
## H.P.verbatim.colon H.P.verbatim.colon -3.194363e-02
## H.npnct06.log H.npnct06.log 3.190718e-02
## S.npnct01.log S.npnct01.log 3.093101e-02
## A.npnct01.log A.npnct01.log 3.093101e-02
## A.T.can A.T.can 3.083389e-02
## H.T.polit H.T.polit -3.058564e-02
## H.npnct16.log H.npnct16.log 3.039622e-02
## S.T.can S.T.can 3.005998e-02
## S.P.metropolitan.diary.colon S.P.metropolitan.diary.colon -2.841404e-02
## A.P.metropolitan.diary.colon A.P.metropolitan.diary.colon -2.841404e-02
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.3 2.798355e-02
## H.T.billion H.T.billion -2.776561e-02
## S.npnct21.log S.npnct21.log 2.760321e-02
## S.npnct23.log S.npnct23.log 2.760321e-02
## H.T.ebola H.T.ebola 2.682920e-02
## A.T.new A.T.new -2.563613e-02
## S.T.new S.T.new -2.558595e-02
## H.T.deal H.T.deal -2.552288e-02
## H.npnct13.log H.npnct13.log -2.524770e-02
## H.T.newyorktim H.T.newyorktim -2.514415e-02
## A.T.time A.T.time -2.430509e-02
## S.T.time S.T.time -2.416246e-02
## A.npnct14.log A.npnct14.log -2.407715e-02
## S.npnct06.log S.npnct06.log -2.389145e-02
## A.npnct06.log A.npnct06.log -2.389145e-02
## A.T.take A.T.take -2.271897e-02
## H.npnct01.log H.npnct01.log 2.271577e-02
## S.T.take S.T.take -2.264447e-02
## H.P.on.this.day H.P.on.this.day -2.150663e-02
## S.npnct14.log S.npnct14.log -2.121844e-02
## H.T.test H.T.test -2.117852e-02
## H.npnct02.log H.npnct02.log -2.001851e-02
## S.npnct20.log S.npnct20.log -1.923169e-02
## A.npnct20.log A.npnct20.log -1.923169e-02
## PubDate.month.fctr PubDate.month.fctr 1.914874e-02
## A.T.said A.T.said 1.876762e-02
## S.T.said S.T.said 1.863436e-02
## A.T.obama A.T.obama -1.847812e-02
## S.T.obama S.T.obama -1.847126e-02
## S.P.first.draft S.P.first.draft -1.841153e-02
## A.P.first.draft A.P.first.draft -1.841153e-02
## S.npnct24.log S.npnct24.log 1.665130e-02
## A.npnct24.log A.npnct24.log 1.665130e-02
## S.P.year.colon S.P.year.colon -1.569779e-02
## A.P.year.colon A.P.year.colon -1.569779e-02
## PubDate.POSIX PubDate.POSIX 1.568326e-02
## PubDate.zoo PubDate.zoo 1.568326e-02
## A.npnct21.log A.npnct21.log 1.537569e-02
## A.npnct23.log A.npnct23.log 1.537569e-02
## A.npnct17.log A.npnct17.log -1.457558e-02
## A.npnct02.log A.npnct02.log -1.451467e-02
## H.T.big H.T.big -1.438162e-02
## H.T.word H.T.word -1.382927e-02
## A.npnct03.log A.npnct03.log -1.359260e-02
## H.T.make H.T.make 1.349595e-02
## H.npnct11.log H.npnct11.log 1.333613e-02
## H.npnct12.log H.npnct12.log -1.305305e-02
## A.P.http A.P.http -1.294748e-02
## A.npnct18.log A.npnct18.log -1.271661e-02
## S.npnct03.log S.npnct03.log -1.240734e-02
## H.npnct07.log H.npnct07.log -1.201741e-02
## PubDate.second.fctr PubDate.second.fctr -1.187946e-02
## PubDate.second.fctr.nonNA PubDate.second.fctr.nonNA -1.187946e-02
## UniqueID UniqueID 1.182492e-02
## PubDate.date.fctr PubDate.date.fctr -1.164756e-02
## PubDate.date.fctr.nonNA PubDate.date.fctr.nonNA -1.164756e-02
## S.npnct07.log S.npnct07.log -1.083506e-02
## A.npnct07.log A.npnct07.log -1.083506e-02
## A.T.one A.T.one 1.051414e-02
## S.T.one S.T.one 1.050293e-02
## H.T.bank H.T.bank -1.037439e-02
## H.T.say H.T.say -9.763205e-03
## H.T.obama H.T.obama -9.682517e-03
## H.P.friday.night.music H.P.friday.night.music -9.653967e-03
## H.npnct05.log H.npnct05.log -9.653967e-03
## H.npnct03.log H.npnct03.log 9.533020e-03
## .rnorm .rnorm -8.244230e-03
## H.P.s.notebook H.P.s.notebook 7.755542e-03
## PubDate.last100.log PubDate.last100.log -7.663322e-03
## H.npnct10.log H.npnct10.log -5.547032e-03
## H.npnct20.log H.npnct20.log -5.547032e-03
## S.npnct02.log S.npnct02.log -5.547032e-03
## S.npnct10.log S.npnct10.log -5.547032e-03
## A.npnct10.log A.npnct10.log -5.547032e-03
## A.npnct25.log A.npnct25.log -5.547032e-03
## A.npnct08.log A.npnct08.log -4.193476e-03
## S.npnct08.log S.npnct08.log -3.372706e-03
## S.T.presid S.T.presid -2.381159e-03
## A.T.presid A.T.presid -2.090565e-03
## S.npnct16.log S.npnct16.log -1.587454e-03
## A.npnct16.log A.npnct16.log -1.587454e-03
## H.T.take H.T.take -1.263270e-03
## H.npnct24.log H.npnct24.log -9.890046e-19
## H.npnct09.log H.npnct09.log NA
## H.npnct17.log H.npnct17.log NA
## H.npnct18.log H.npnct18.log NA
## H.npnct21.log H.npnct21.log NA
## H.npnct22.log H.npnct22.log NA
## H.npnct23.log H.npnct23.log NA
## H.npnct25.log H.npnct25.log NA
## H.npnct26.log H.npnct26.log NA
## H.npnct27.log H.npnct27.log NA
## H.npnct29.log H.npnct29.log NA
## H.npnct30.log H.npnct30.log NA
## H.P.http H.P.http NA
## S.npnct05.log S.npnct05.log NA
## S.npnct09.log S.npnct09.log NA
## S.npnct17.log S.npnct17.log NA
## S.npnct18.log S.npnct18.log NA
## S.npnct22.log S.npnct22.log NA
## S.npnct25.log S.npnct25.log NA
## S.npnct26.log S.npnct26.log NA
## S.npnct27.log S.npnct27.log NA
## S.npnct29.log S.npnct29.log NA
## S.npnct30.log S.npnct30.log NA
## S.P.http S.P.http NA
## A.npnct05.log A.npnct05.log NA
## A.npnct09.log A.npnct09.log NA
## A.npnct22.log A.npnct22.log NA
## A.npnct26.log A.npnct26.log NA
## A.npnct27.log A.npnct27.log NA
## A.npnct29.log A.npnct29.log NA
## A.npnct30.log A.npnct30.log NA
## PubDate.year.fctr PubDate.year.fctr NA
## exclude.as.feat cor.y.abs
## Popular 1 1.000000e+00
## A.nuppr.log 0 2.685472e-01
## S.nuppr.log 0 2.682841e-01
## WordCount.log 1 2.675605e-01
## A.ratio.sum.TfIdf.nwrds 0 2.653142e-01
## WordCount.log.nonNA 0 2.652413e-01
## S.ratio.sum.TfIdf.nwrds 0 2.651816e-01
## WordCount 1 2.575265e-01
## S.nwrds.unq.log 0 2.373282e-01
## A.nwrds.unq.log 0 2.372695e-01
## H.ratio.sum.TfIdf.nwrds 0 2.254479e-01
## A.nchrs.log 0 2.054874e-01
## S.nchrs.log 0 2.054030e-01
## H.nwrds.unq.log 0 2.013666e-01
## A.nwrds.log 0 1.895162e-01
## S.nwrds.log 0 1.893755e-01
## H.nchrs.log 0 1.710624e-01
## myCategory.fctr 1 1.703175e-01
## myCategory.fctr.nonNA 0 1.703175e-01
## H.nwrds.log 0 1.573431e-01
## PubDate.day.minutes.poly.1 0 1.567535e-01
## H.sum.TfIdf 0 1.519219e-01
## S.sum.TfIdf 0 1.469053e-01
## A.sum.TfIdf 0 1.462756e-01
## PubDate.hour.fctr 1 1.354368e-01
## PubDate.hour.fctr.nonNA 0 1.354368e-01
## H.npnct19.log 0 1.283641e-01
## H.nuppr.log 0 1.278085e-01
## A.ndgts.log 0 1.246678e-01
## S.ndgts.log 0 1.239225e-01
## H.ndgts.log 0 1.196633e-01
## A.nstopwrds.log 0 1.144762e-01
## S.nstopwrds.log 0 1.138992e-01
## A.ratio.nstopwrds.nwrds 0 1.112093e-01
## S.ratio.nstopwrds.nwrds 0 1.105792e-01
## PubDate.wkend 0 1.067288e-01
## A.npnct11.log 0 9.183870e-02
## S.npnct11.log 0 9.158156e-02
## H.P.recap.colon 1 9.008096e-02
## H.npnct28.log 0 8.917338e-02
## H.P.quandary 1 8.734922e-02
## H.nstopwrds.log 0 8.657067e-02
## .clusterid 1 8.644104e-02
## .clusterid.fctr 0 8.644104e-02
## S.T.week 1 8.552704e-02
## A.T.week 1 8.542792e-02
## S.T.fashion 1 8.417159e-02
## A.T.fashion 1 8.416793e-02
## H.npnct15.log 0 8.273237e-02
## H.T.fashion 1 7.946462e-02
## H.P.year.colon 1 7.842875e-02
## H.P.fashion.week 1 7.632046e-02
## PubDate.day.minutes.poly.4 0 7.394139e-02
## S.T.archiv 1 7.202808e-02
## A.T.archiv 1 7.202808e-02
## PubDate.day.minutes.poly.2 0 7.097772e-02
## S.P.fashion.week 1 7.080716e-02
## A.P.fashion.week 1 7.080716e-02
## S.T.tribun 1 7.013418e-02
## A.T.tribun 1 7.013418e-02
## S.T.intern 1 6.956906e-02
## A.T.intern 1 6.953025e-02
## S.T.photo 1 6.874283e-02
## A.T.photo 1 6.873838e-02
## H.T.week 1 6.827601e-02
## S.T.herald 1 6.752419e-02
## A.T.herald 1 6.752419e-02
## A.npnct15.log 0 6.735974e-02
## S.npnct15.log 0 6.612020e-02
## H.T.X2015 1 6.601141e-02
## H.T.daili 1 6.303731e-02
## S.npnct04.log 0 6.294642e-02
## A.npnct04.log 0 6.294642e-02
## H.T.report 1 6.238114e-02
## S.T.diari 1 6.229931e-02
## A.T.diari 1 6.229931e-02
## H.npnct14.log 0 6.158577e-02
## H.P.no.comment.colon 1 6.074669e-02
## H.T.day 1 6.044381e-02
## H.T.springsumm 1 5.943248e-02
## H.T.today 1 5.833786e-02
## PubDate.day.minutes.poly.5 0 5.592923e-02
## H.T.newyork 1 5.563086e-02
## S.npnct19.log 0 5.552543e-02
## A.npnct19.log 0 5.531065e-02
## S.T.articl 1 5.471737e-02
## A.T.articl 1 5.470831e-02
## H.P.facts.figures 1 5.410097e-02
## PubDate.last10 1 5.398093e-02
## H.npnct08.log 0 5.375262e-02
## S.npnct13.log 0 5.332519e-02
## H.npnct04.log 0 5.126277e-02
## S.T.share 1 5.105597e-02
## A.T.share 1 5.105597e-02
## A.npnct13.log 0 4.999563e-02
## S.T.newyorktim 1 4.985328e-02
## A.T.newyorktim 1 4.984782e-02
## PubDate.last10.log 0 4.931702e-02
## H.T.busi 1 4.899819e-02
## S.T.editor 1 4.875454e-02
## A.T.editor 1 4.875454e-02
## H.T.morn 1 4.838380e-02
## S.T.word 1 4.822452e-02
## A.T.word 1 4.821561e-02
## S.T.compani 1 4.787994e-02
## S.T.report 1 4.778378e-02
## A.T.compani 1 4.774812e-02
## A.T.report 1 4.773092e-02
## S.T.newyork 1 4.694998e-02
## A.T.newyork 1 4.686921e-02
## PubDate.last1.log 0 4.635751e-02
## H.T.X2014 1 4.522109e-02
## H.T.first 1 4.472902e-02
## H.T.news 1 4.436368e-02
## H.P.readers.respond 1 4.432886e-02
## H.T.clip 1 4.388279e-02
## H.P.daily.clip.report 1 4.388279e-02
## S.P.daily.clip.report 1 4.388279e-02
## A.P.daily.clip.report 1 4.388279e-02
## A.npnct28.log 0 4.373349e-02
## S.npnct28.log 0 4.370037e-02
## H.P.first.draft 1 4.316253e-02
## S.T.first 1 4.302271e-02
## A.T.first 1 4.288090e-02
## H.P.today.in.smallbusiness 1 4.243051e-02
## A.T.day 1 4.234061e-02
## S.T.day 1 4.225390e-02
## A.T.show 1 4.185292e-02
## S.T.show 1 4.182920e-02
## S.T.senat 1 4.143422e-02
## A.T.senat 1 4.139980e-02
## A.T.make 1 4.124187e-02
## S.T.make 1 4.118050e-02
## H.T.new 1 4.111696e-02
## H.ratio.nstopwrds.nwrds 0 4.024406e-02
## H.T.pictur 1 4.003882e-02
## PubDate.last100 1 3.989229e-02
## PubDate.wkday.fctr 1 3.980129e-02
## PubDate.wkday.fctr.nonNA 0 3.980129e-02
## S.T.appear 1 3.941362e-02
## A.T.appear 1 3.941362e-02
## S.T.will 1 3.850299e-02
## A.T.will 1 3.845737e-02
## H.P.what.we.are 1 3.775209e-02
## A.npnct12.log 0 3.760012e-02
## S.T.year 1 3.756011e-02
## A.T.year 1 3.741571e-02
## H.P.today.in.politic 1 3.733661e-02
## S.npnct12.log 0 3.638891e-02
## PubDate.last1 1 3.592267e-02
## H.T.read 1 3.467043e-02
## PubDate.minute.fctr 1 3.407385e-02
## PubDate.minute.fctr.nonNA 0 3.407385e-02
## H.T.get 1 3.300192e-02
## H.T.art 1 3.291486e-02
## H.T.china 1 3.283653e-02
## H.P.verbatim.colon 1 3.194363e-02
## H.npnct06.log 0 3.190718e-02
## S.npnct01.log 0 3.093101e-02
## A.npnct01.log 0 3.093101e-02
## A.T.can 1 3.083389e-02
## H.T.polit 1 3.058564e-02
## H.npnct16.log 0 3.039622e-02
## S.T.can 1 3.005998e-02
## S.P.metropolitan.diary.colon 1 2.841404e-02
## A.P.metropolitan.diary.colon 1 2.841404e-02
## PubDate.day.minutes.poly.3 0 2.798355e-02
## H.T.billion 1 2.776561e-02
## S.npnct21.log 0 2.760321e-02
## S.npnct23.log 0 2.760321e-02
## H.T.ebola 1 2.682920e-02
## A.T.new 1 2.563613e-02
## S.T.new 1 2.558595e-02
## H.T.deal 1 2.552288e-02
## H.npnct13.log 0 2.524770e-02
## H.T.newyorktim 1 2.514415e-02
## A.T.time 1 2.430509e-02
## S.T.time 1 2.416246e-02
## A.npnct14.log 0 2.407715e-02
## S.npnct06.log 0 2.389145e-02
## A.npnct06.log 0 2.389145e-02
## A.T.take 1 2.271897e-02
## H.npnct01.log 0 2.271577e-02
## S.T.take 1 2.264447e-02
## H.P.on.this.day 1 2.150663e-02
## S.npnct14.log 0 2.121844e-02
## H.T.test 1 2.117852e-02
## H.npnct02.log 0 2.001851e-02
## S.npnct20.log 0 1.923169e-02
## A.npnct20.log 0 1.923169e-02
## PubDate.month.fctr 1 1.914874e-02
## A.T.said 1 1.876762e-02
## S.T.said 1 1.863436e-02
## A.T.obama 1 1.847812e-02
## S.T.obama 1 1.847126e-02
## S.P.first.draft 1 1.841153e-02
## A.P.first.draft 1 1.841153e-02
## S.npnct24.log 0 1.665130e-02
## A.npnct24.log 0 1.665130e-02
## S.P.year.colon 1 1.569779e-02
## A.P.year.colon 1 1.569779e-02
## PubDate.POSIX 1 1.568326e-02
## PubDate.zoo 1 1.568326e-02
## A.npnct21.log 0 1.537569e-02
## A.npnct23.log 0 1.537569e-02
## A.npnct17.log 0 1.457558e-02
## A.npnct02.log 0 1.451467e-02
## H.T.big 1 1.438162e-02
## H.T.word 1 1.382927e-02
## A.npnct03.log 0 1.359260e-02
## H.T.make 1 1.349595e-02
## H.npnct11.log 0 1.333613e-02
## H.npnct12.log 0 1.305305e-02
## A.P.http 1 1.294748e-02
## A.npnct18.log 0 1.271661e-02
## S.npnct03.log 0 1.240734e-02
## H.npnct07.log 0 1.201741e-02
## PubDate.second.fctr 1 1.187946e-02
## PubDate.second.fctr.nonNA 0 1.187946e-02
## UniqueID 1 1.182492e-02
## PubDate.date.fctr 1 1.164756e-02
## PubDate.date.fctr.nonNA 0 1.164756e-02
## S.npnct07.log 0 1.083506e-02
## A.npnct07.log 0 1.083506e-02
## A.T.one 1 1.051414e-02
## S.T.one 1 1.050293e-02
## H.T.bank 1 1.037439e-02
## H.T.say 1 9.763205e-03
## H.T.obama 1 9.682517e-03
## H.P.friday.night.music 1 9.653967e-03
## H.npnct05.log 0 9.653967e-03
## H.npnct03.log 0 9.533020e-03
## .rnorm 0 8.244230e-03
## H.P.s.notebook 1 7.755542e-03
## PubDate.last100.log 0 7.663322e-03
## H.npnct10.log 0 5.547032e-03
## H.npnct20.log 0 5.547032e-03
## S.npnct02.log 0 5.547032e-03
## S.npnct10.log 0 5.547032e-03
## A.npnct10.log 0 5.547032e-03
## A.npnct25.log 0 5.547032e-03
## A.npnct08.log 0 4.193476e-03
## S.npnct08.log 0 3.372706e-03
## S.T.presid 1 2.381159e-03
## A.T.presid 1 2.090565e-03
## S.npnct16.log 0 1.587454e-03
## A.npnct16.log 0 1.587454e-03
## H.T.take 1 1.263270e-03
## H.npnct24.log 0 9.890046e-19
## H.npnct09.log 0 NA
## H.npnct17.log 0 NA
## H.npnct18.log 0 NA
## H.npnct21.log 0 NA
## H.npnct22.log 0 NA
## H.npnct23.log 0 NA
## H.npnct25.log 0 NA
## H.npnct26.log 0 NA
## H.npnct27.log 0 NA
## H.npnct29.log 0 NA
## H.npnct30.log 0 NA
## H.P.http 1 NA
## S.npnct05.log 0 NA
## S.npnct09.log 0 NA
## S.npnct17.log 0 NA
## S.npnct18.log 0 NA
## S.npnct22.log 0 NA
## S.npnct25.log 0 NA
## S.npnct26.log 0 NA
## S.npnct27.log 0 NA
## S.npnct29.log 0 NA
## S.npnct30.log 0 NA
## S.P.http 1 NA
## A.npnct05.log 0 NA
## A.npnct09.log 0 NA
## A.npnct22.log 0 NA
## A.npnct26.log 0 NA
## A.npnct27.log 0 NA
## A.npnct29.log 0 NA
## A.npnct30.log 0 NA
## PubDate.year.fctr 0 NA# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(A.npnct01.log, S.npnct01.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct01.log)=0.0309"
## [1] "cor(Popular.fctr, S.npnct01.log)=0.0309"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.npnct01.log as highly correlated with
## A.npnct01.log## [1] "cor(A.npnct04.log, S.npnct04.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct04.log)=-0.0629"
## [1] "cor(Popular.fctr, S.npnct04.log)=-0.0629"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.npnct04.log as highly correlated with
## A.npnct04.log## [1] "cor(A.npnct06.log, S.npnct06.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct06.log)=-0.0239"
## [1] "cor(Popular.fctr, S.npnct06.log)=-0.0239"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.npnct06.log as highly correlated with
## A.npnct06.log## [1] "cor(A.npnct07.log, S.npnct07.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct07.log)=-0.0108"
## [1] "cor(Popular.fctr, S.npnct07.log)=-0.0108"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.npnct07.log as highly correlated with
## A.npnct07.log## [1] "cor(A.npnct20.log, S.npnct20.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct20.log)=-0.0192"
## [1] "cor(Popular.fctr, S.npnct20.log)=-0.0192"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.npnct20.log as highly correlated with
## A.npnct20.log## [1] "cor(A.npnct21.log, A.npnct23.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct21.log)=0.0154"
## [1] "cor(Popular.fctr, A.npnct23.log)=0.0154"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified A.npnct23.log as highly correlated with
## A.npnct21.log## [1] "cor(A.npnct24.log, S.npnct24.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct24.log)=0.0167"
## [1] "cor(Popular.fctr, S.npnct24.log)=0.0167"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.npnct24.log as highly correlated with
## A.npnct24.log## [1] "cor(S.npnct21.log, S.npnct23.log)=1.0000"
## [1] "cor(Popular.fctr, S.npnct21.log)=0.0276"
## [1] "cor(Popular.fctr, S.npnct23.log)=0.0276"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.npnct23.log as highly correlated with
## S.npnct21.log## [1] "cor(A.ratio.sum.TfIdf.nwrds, S.ratio.sum.TfIdf.nwrds)=0.9999"
## [1] "cor(Popular.fctr, A.ratio.sum.TfIdf.nwrds)=0.2653"
## [1] "cor(Popular.fctr, S.ratio.sum.TfIdf.nwrds)=0.2652"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.ratio.sum.TfIdf.nwrds as highly correlated
## with A.ratio.sum.TfIdf.nwrds## [1] "cor(A.npnct11.log, S.npnct11.log)=0.9997"
## [1] "cor(Popular.fctr, A.npnct11.log)=-0.0918"
## [1] "cor(Popular.fctr, S.npnct11.log)=-0.0916"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.npnct11.log as highly correlated with
## A.npnct11.log## [1] "cor(A.sum.TfIdf, S.sum.TfIdf)=0.9997"
## [1] "cor(Popular.fctr, A.sum.TfIdf)=0.1463"
## [1] "cor(Popular.fctr, S.sum.TfIdf)=0.1469"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified A.sum.TfIdf as highly correlated with
## S.sum.TfIdf## [1] "cor(A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds)=0.9994"
## [1] "cor(Popular.fctr, A.ratio.nstopwrds.nwrds)=0.1112"
## [1] "cor(Popular.fctr, S.ratio.nstopwrds.nwrds)=0.1106"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.ratio.nstopwrds.nwrds as highly correlated
## with A.ratio.nstopwrds.nwrds## [1] "cor(A.nuppr.log, S.nuppr.log)=0.9991"
## [1] "cor(Popular.fctr, A.nuppr.log)=-0.2685"
## [1] "cor(Popular.fctr, S.nuppr.log)=-0.2683"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.nuppr.log as highly correlated with
## A.nuppr.log## [1] "cor(A.nwrds.unq.log, S.nwrds.unq.log)=0.9990"
## [1] "cor(Popular.fctr, A.nwrds.unq.log)=-0.2373"
## [1] "cor(Popular.fctr, S.nwrds.unq.log)=-0.2373"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified A.nwrds.unq.log as highly correlated with
## S.nwrds.unq.log## [1] "cor(A.npnct28.log, S.npnct28.log)=0.9989"
## [1] "cor(Popular.fctr, A.npnct28.log)=-0.0437"
## [1] "cor(Popular.fctr, S.npnct28.log)=-0.0437"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.npnct28.log as highly correlated with
## A.npnct28.log## [1] "cor(A.nstopwrds.log, S.nstopwrds.log)=0.9989"
## [1] "cor(Popular.fctr, A.nstopwrds.log)=-0.1145"
## [1] "cor(Popular.fctr, S.nstopwrds.log)=-0.1139"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.nstopwrds.log as highly correlated with
## A.nstopwrds.log## [1] "cor(A.nchrs.log, S.nchrs.log)=0.9988"
## [1] "cor(Popular.fctr, A.nchrs.log)=-0.2055"
## [1] "cor(Popular.fctr, S.nchrs.log)=-0.2054"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.nchrs.log as highly correlated with
## A.nchrs.log## [1] "cor(A.nwrds.log, S.nwrds.log)=0.9985"
## [1] "cor(Popular.fctr, A.nwrds.log)=-0.1895"
## [1] "cor(Popular.fctr, S.nwrds.log)=-0.1894"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.nwrds.log as highly correlated with
## A.nwrds.log## [1] "cor(A.npnct19.log, S.npnct19.log)=0.9957"
## [1] "cor(Popular.fctr, A.npnct19.log)=0.0553"
## [1] "cor(Popular.fctr, S.npnct19.log)=0.0555"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified A.npnct19.log as highly correlated with
## S.npnct19.log## [1] "cor(A.ndgts.log, S.ndgts.log)=0.9955"
## [1] "cor(Popular.fctr, A.ndgts.log)=-0.1247"
## [1] "cor(Popular.fctr, S.ndgts.log)=-0.1239"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.ndgts.log as highly correlated with
## A.ndgts.log## [1] "cor(A.npnct12.log, S.npnct12.log)=0.9935"
## [1] "cor(Popular.fctr, A.npnct12.log)=-0.0376"
## [1] "cor(Popular.fctr, S.npnct12.log)=-0.0364"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.npnct12.log as highly correlated with
## A.npnct12.log## [1] "cor(A.npnct15.log, S.npnct15.log)=0.9916"
## [1] "cor(Popular.fctr, A.npnct15.log)=-0.0674"
## [1] "cor(Popular.fctr, S.npnct15.log)=-0.0661"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.npnct15.log as highly correlated with
## A.npnct15.log## [1] "cor(A.npnct13.log, S.npnct13.log)=0.9795"
## [1] "cor(Popular.fctr, A.npnct13.log)=-0.0500"
## [1] "cor(Popular.fctr, S.npnct13.log)=-0.0533"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified A.npnct13.log as highly correlated with
## S.npnct13.log## [1] "cor(A.nchrs.log, A.nwrds.log)=0.9691"
## [1] "cor(Popular.fctr, A.nchrs.log)=-0.2055"
## [1] "cor(Popular.fctr, A.nwrds.log)=-0.1895"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified A.nwrds.log as highly correlated with
## A.nchrs.log## [1] "cor(A.nchrs.log, S.nwrds.unq.log)=0.9472"
## [1] "cor(Popular.fctr, A.nchrs.log)=-0.2055"
## [1] "cor(Popular.fctr, S.nwrds.unq.log)=-0.2373"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified A.nchrs.log as highly correlated with
## S.nwrds.unq.log## [1] "cor(H.nchrs.log, H.nwrds.log)=0.9212"
## [1] "cor(Popular.fctr, H.nchrs.log)=-0.1711"
## [1] "cor(Popular.fctr, H.nwrds.log)=-0.1573"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified H.nwrds.log as highly correlated with
## H.nchrs.log## [1] "cor(A.npnct03.log, S.npnct03.log)=0.9128"
## [1] "cor(Popular.fctr, A.npnct03.log)=-0.0136"
## [1] "cor(Popular.fctr, S.npnct03.log)=-0.0124"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.npnct03.log as highly correlated with
## A.npnct03.log## [1] "cor(A.npnct02.log, A.npnct18.log)=0.9125"
## [1] "cor(Popular.fctr, A.npnct02.log)=-0.0145"
## [1] "cor(Popular.fctr, A.npnct18.log)=-0.0127"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified A.npnct18.log as highly correlated with
## A.npnct02.log## [1] "cor(PubDate.day.minutes.poly.1, PubDate.hour.fctr.nonNA)=0.9026"
## [1] "cor(Popular.fctr, PubDate.day.minutes.poly.1)=0.1568"
## [1] "cor(Popular.fctr, PubDate.hour.fctr.nonNA)=0.1354"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified PubDate.hour.fctr.nonNA as highly correlated
## with PubDate.day.minutes.poly.1## [1] "cor(H.nchrs.log, H.nwrds.unq.log)=0.8882"
## [1] "cor(Popular.fctr, H.nchrs.log)=-0.1711"
## [1] "cor(Popular.fctr, H.nwrds.unq.log)=-0.2014"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified H.nchrs.log as highly correlated with
## H.nwrds.unq.log## [1] "cor(A.npnct02.log, A.npnct17.log)=0.8745"
## [1] "cor(Popular.fctr, A.npnct02.log)=-0.0145"
## [1] "cor(Popular.fctr, A.npnct17.log)=-0.0146"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified A.npnct02.log as highly correlated with
## A.npnct17.log## [1] "cor(H.nuppr.log, H.nwrds.unq.log)=0.8294"
## [1] "cor(Popular.fctr, H.nuppr.log)=-0.1278"
## [1] "cor(Popular.fctr, H.nwrds.unq.log)=-0.2014"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified H.nuppr.log as highly correlated with
## H.nwrds.unq.log## [1] "cor(H.npnct06.log, H.npnct16.log)=0.8106"
## [1] "cor(Popular.fctr, H.npnct06.log)=0.0319"
## [1] "cor(Popular.fctr, H.npnct16.log)=0.0304"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified H.npnct16.log as highly correlated with
## H.npnct06.log## [1] "cor(A.npnct14.log, A.npnct17.log)=0.7663"
## [1] "cor(Popular.fctr, A.npnct14.log)=-0.0241"
## [1] "cor(Popular.fctr, A.npnct17.log)=-0.0146"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified A.npnct17.log as highly correlated with
## A.npnct14.log## [1] "cor(A.npnct21.log, S.npnct21.log)=0.7461"
## [1] "cor(Popular.fctr, A.npnct21.log)=0.0154"
## [1] "cor(Popular.fctr, S.npnct21.log)=0.0276"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified A.npnct21.log as highly correlated with
## S.npnct21.log## [1] "cor(A.ratio.sum.TfIdf.nwrds, S.nwrds.unq.log)=-0.7362"
## [1] "cor(Popular.fctr, A.ratio.sum.TfIdf.nwrds)=0.2653"
## [1] "cor(Popular.fctr, S.nwrds.unq.log)=-0.2373"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified S.nwrds.unq.log as highly correlated with
## A.ratio.sum.TfIdf.nwrds## id cor.y
## Popular Popular 1.000000e+00
## WordCount.log WordCount.log 2.675605e-01
## A.ratio.sum.TfIdf.nwrds A.ratio.sum.TfIdf.nwrds 2.653142e-01
## WordCount.log.nonNA WordCount.log.nonNA 2.652413e-01
## S.ratio.sum.TfIdf.nwrds S.ratio.sum.TfIdf.nwrds 2.651816e-01
## WordCount WordCount 2.575265e-01
## H.ratio.sum.TfIdf.nwrds H.ratio.sum.TfIdf.nwrds 2.254479e-01
## myCategory.fctr myCategory.fctr 1.703175e-01
## myCategory.fctr.nonNA myCategory.fctr.nonNA 1.703175e-01
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.1 1.567535e-01
## H.sum.TfIdf H.sum.TfIdf 1.519219e-01
## S.sum.TfIdf S.sum.TfIdf 1.469053e-01
## A.sum.TfIdf A.sum.TfIdf 1.462756e-01
## PubDate.hour.fctr PubDate.hour.fctr 1.354368e-01
## PubDate.hour.fctr.nonNA PubDate.hour.fctr.nonNA 1.354368e-01
## H.npnct19.log H.npnct19.log 1.283641e-01
## A.ratio.nstopwrds.nwrds A.ratio.nstopwrds.nwrds 1.112093e-01
## S.ratio.nstopwrds.nwrds S.ratio.nstopwrds.nwrds 1.105792e-01
## PubDate.wkend PubDate.wkend 1.067288e-01
## H.P.recap.colon H.P.recap.colon 9.008096e-02
## H.P.quandary H.P.quandary 8.734922e-02
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.4 7.394139e-02
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.2 7.097772e-02
## H.P.no.comment.colon H.P.no.comment.colon 6.074669e-02
## S.npnct19.log S.npnct19.log 5.552543e-02
## A.npnct19.log A.npnct19.log 5.531065e-02
## H.P.facts.figures H.P.facts.figures 5.410097e-02
## PubDate.last10 PubDate.last10 5.398093e-02
## H.npnct08.log H.npnct08.log 5.375262e-02
## PubDate.last10.log PubDate.last10.log 4.931702e-02
## PubDate.last1.log PubDate.last1.log 4.635751e-02
## H.P.readers.respond H.P.readers.respond 4.432886e-02
## A.T.make A.T.make 4.124187e-02
## S.T.make S.T.make 4.118050e-02
## H.ratio.nstopwrds.nwrds H.ratio.nstopwrds.nwrds 4.024406e-02
## PubDate.last100 PubDate.last100 3.989229e-02
## PubDate.last1 PubDate.last1 3.592267e-02
## H.T.get H.T.get 3.300192e-02
## H.npnct06.log H.npnct06.log 3.190718e-02
## A.npnct01.log A.npnct01.log 3.093101e-02
## S.npnct01.log S.npnct01.log 3.093101e-02
## A.T.can A.T.can 3.083389e-02
## H.npnct16.log H.npnct16.log 3.039622e-02
## S.T.can S.T.can 3.005998e-02
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.3 2.798355e-02
## S.npnct21.log S.npnct21.log 2.760321e-02
## S.npnct23.log S.npnct23.log 2.760321e-02
## H.T.ebola H.T.ebola 2.682920e-02
## H.npnct01.log H.npnct01.log 2.271577e-02
## PubDate.month.fctr PubDate.month.fctr 1.914874e-02
## A.T.said A.T.said 1.876762e-02
## S.T.said S.T.said 1.863436e-02
## A.npnct24.log A.npnct24.log 1.665130e-02
## S.npnct24.log S.npnct24.log 1.665130e-02
## PubDate.POSIX PubDate.POSIX 1.568326e-02
## PubDate.zoo PubDate.zoo 1.568326e-02
## A.npnct21.log A.npnct21.log 1.537569e-02
## A.npnct23.log A.npnct23.log 1.537569e-02
## H.T.make H.T.make 1.349595e-02
## H.npnct11.log H.npnct11.log 1.333613e-02
## UniqueID UniqueID 1.182492e-02
## A.T.one A.T.one 1.051414e-02
## S.T.one S.T.one 1.050293e-02
## H.npnct03.log H.npnct03.log 9.533020e-03
## H.P.s.notebook H.P.s.notebook 7.755542e-03
## H.npnct24.log H.npnct24.log -9.890046e-19
## H.T.take H.T.take -1.263270e-03
## A.npnct16.log A.npnct16.log -1.587454e-03
## S.npnct16.log S.npnct16.log -1.587454e-03
## A.T.presid A.T.presid -2.090565e-03
## S.T.presid S.T.presid -2.381159e-03
## S.npnct08.log S.npnct08.log -3.372706e-03
## A.npnct08.log A.npnct08.log -4.193476e-03
## A.npnct25.log A.npnct25.log -5.547032e-03
## A.npnct10.log A.npnct10.log -5.547032e-03
## H.npnct10.log H.npnct10.log -5.547032e-03
## H.npnct20.log H.npnct20.log -5.547032e-03
## S.npnct02.log S.npnct02.log -5.547032e-03
## S.npnct10.log S.npnct10.log -5.547032e-03
## PubDate.last100.log PubDate.last100.log -7.663322e-03
## .rnorm .rnorm -8.244230e-03
## H.npnct05.log H.npnct05.log -9.653967e-03
## H.P.friday.night.music H.P.friday.night.music -9.653967e-03
## H.T.obama H.T.obama -9.682517e-03
## H.T.say H.T.say -9.763205e-03
## H.T.bank H.T.bank -1.037439e-02
## A.npnct07.log A.npnct07.log -1.083506e-02
## S.npnct07.log S.npnct07.log -1.083506e-02
## PubDate.date.fctr PubDate.date.fctr -1.164756e-02
## PubDate.date.fctr.nonNA PubDate.date.fctr.nonNA -1.164756e-02
## PubDate.second.fctr PubDate.second.fctr -1.187946e-02
## PubDate.second.fctr.nonNA PubDate.second.fctr.nonNA -1.187946e-02
## H.npnct07.log H.npnct07.log -1.201741e-02
## S.npnct03.log S.npnct03.log -1.240734e-02
## A.npnct18.log A.npnct18.log -1.271661e-02
## A.P.http A.P.http -1.294748e-02
## H.npnct12.log H.npnct12.log -1.305305e-02
## A.npnct03.log A.npnct03.log -1.359260e-02
## H.T.word H.T.word -1.382927e-02
## H.T.big H.T.big -1.438162e-02
## A.npnct02.log A.npnct02.log -1.451467e-02
## A.npnct17.log A.npnct17.log -1.457558e-02
## A.P.year.colon A.P.year.colon -1.569779e-02
## S.P.year.colon S.P.year.colon -1.569779e-02
## A.P.first.draft A.P.first.draft -1.841153e-02
## S.P.first.draft S.P.first.draft -1.841153e-02
## S.T.obama S.T.obama -1.847126e-02
## A.T.obama A.T.obama -1.847812e-02
## A.npnct20.log A.npnct20.log -1.923169e-02
## S.npnct20.log S.npnct20.log -1.923169e-02
## H.npnct02.log H.npnct02.log -2.001851e-02
## H.T.test H.T.test -2.117852e-02
## S.npnct14.log S.npnct14.log -2.121844e-02
## H.P.on.this.day H.P.on.this.day -2.150663e-02
## S.T.take S.T.take -2.264447e-02
## A.T.take A.T.take -2.271897e-02
## A.npnct06.log A.npnct06.log -2.389145e-02
## S.npnct06.log S.npnct06.log -2.389145e-02
## A.npnct14.log A.npnct14.log -2.407715e-02
## S.T.time S.T.time -2.416246e-02
## A.T.time A.T.time -2.430509e-02
## H.T.newyorktim H.T.newyorktim -2.514415e-02
## H.npnct13.log H.npnct13.log -2.524770e-02
## H.T.deal H.T.deal -2.552288e-02
## S.T.new S.T.new -2.558595e-02
## A.T.new A.T.new -2.563613e-02
## H.T.billion H.T.billion -2.776561e-02
## A.P.metropolitan.diary.colon A.P.metropolitan.diary.colon -2.841404e-02
## S.P.metropolitan.diary.colon S.P.metropolitan.diary.colon -2.841404e-02
## H.T.polit H.T.polit -3.058564e-02
## H.P.verbatim.colon H.P.verbatim.colon -3.194363e-02
## H.T.china H.T.china -3.283653e-02
## H.T.art H.T.art -3.291486e-02
## PubDate.minute.fctr PubDate.minute.fctr -3.407385e-02
## PubDate.minute.fctr.nonNA PubDate.minute.fctr.nonNA -3.407385e-02
## H.T.read H.T.read -3.467043e-02
## S.npnct12.log S.npnct12.log -3.638891e-02
## H.P.today.in.politic H.P.today.in.politic -3.733661e-02
## A.T.year A.T.year -3.741571e-02
## S.T.year S.T.year -3.756011e-02
## A.npnct12.log A.npnct12.log -3.760012e-02
## H.P.what.we.are H.P.what.we.are -3.775209e-02
## A.T.will A.T.will -3.845737e-02
## S.T.will S.T.will -3.850299e-02
## A.T.appear A.T.appear -3.941362e-02
## S.T.appear S.T.appear -3.941362e-02
## PubDate.wkday.fctr PubDate.wkday.fctr -3.980129e-02
## PubDate.wkday.fctr.nonNA PubDate.wkday.fctr.nonNA -3.980129e-02
## H.T.pictur H.T.pictur -4.003882e-02
## H.T.new H.T.new -4.111696e-02
## A.T.senat A.T.senat -4.139980e-02
## S.T.senat S.T.senat -4.143422e-02
## S.T.show S.T.show -4.182920e-02
## A.T.show A.T.show -4.185292e-02
## S.T.day S.T.day -4.225390e-02
## A.T.day A.T.day -4.234061e-02
## H.P.today.in.smallbusiness H.P.today.in.smallbusiness -4.243051e-02
## A.T.first A.T.first -4.288090e-02
## S.T.first S.T.first -4.302271e-02
## H.P.first.draft H.P.first.draft -4.316253e-02
## S.npnct28.log S.npnct28.log -4.370037e-02
## A.npnct28.log A.npnct28.log -4.373349e-02
## A.P.daily.clip.report A.P.daily.clip.report -4.388279e-02
## H.P.daily.clip.report H.P.daily.clip.report -4.388279e-02
## H.T.clip H.T.clip -4.388279e-02
## S.P.daily.clip.report S.P.daily.clip.report -4.388279e-02
## H.T.news H.T.news -4.436368e-02
## H.T.first H.T.first -4.472902e-02
## H.T.X2014 H.T.X2014 -4.522109e-02
## A.T.newyork A.T.newyork -4.686921e-02
## S.T.newyork S.T.newyork -4.694998e-02
## A.T.report A.T.report -4.773092e-02
## A.T.compani A.T.compani -4.774812e-02
## S.T.report S.T.report -4.778378e-02
## S.T.compani S.T.compani -4.787994e-02
## A.T.word A.T.word -4.821561e-02
## S.T.word S.T.word -4.822452e-02
## H.T.morn H.T.morn -4.838380e-02
## A.T.editor A.T.editor -4.875454e-02
## S.T.editor S.T.editor -4.875454e-02
## H.T.busi H.T.busi -4.899819e-02
## A.T.newyorktim A.T.newyorktim -4.984782e-02
## S.T.newyorktim S.T.newyorktim -4.985328e-02
## A.npnct13.log A.npnct13.log -4.999563e-02
## A.T.share A.T.share -5.105597e-02
## S.T.share S.T.share -5.105597e-02
## H.npnct04.log H.npnct04.log -5.126277e-02
## S.npnct13.log S.npnct13.log -5.332519e-02
## A.T.articl A.T.articl -5.470831e-02
## S.T.articl S.T.articl -5.471737e-02
## H.T.newyork H.T.newyork -5.563086e-02
## PubDate.day.minutes.poly.5 PubDate.day.minutes.poly.5 -5.592923e-02
## H.T.today H.T.today -5.833786e-02
## H.T.springsumm H.T.springsumm -5.943248e-02
## H.T.day H.T.day -6.044381e-02
## H.npnct14.log H.npnct14.log -6.158577e-02
## A.T.diari A.T.diari -6.229931e-02
## S.T.diari S.T.diari -6.229931e-02
## H.T.report H.T.report -6.238114e-02
## A.npnct04.log A.npnct04.log -6.294642e-02
## S.npnct04.log S.npnct04.log -6.294642e-02
## H.T.daili H.T.daili -6.303731e-02
## H.T.X2015 H.T.X2015 -6.601141e-02
## S.npnct15.log S.npnct15.log -6.612020e-02
## A.npnct15.log A.npnct15.log -6.735974e-02
## A.T.herald A.T.herald -6.752419e-02
## S.T.herald S.T.herald -6.752419e-02
## H.T.week H.T.week -6.827601e-02
## A.T.photo A.T.photo -6.873838e-02
## S.T.photo S.T.photo -6.874283e-02
## A.T.intern A.T.intern -6.953025e-02
## S.T.intern S.T.intern -6.956906e-02
## A.T.tribun A.T.tribun -7.013418e-02
## S.T.tribun S.T.tribun -7.013418e-02
## A.P.fashion.week A.P.fashion.week -7.080716e-02
## S.P.fashion.week S.P.fashion.week -7.080716e-02
## A.T.archiv A.T.archiv -7.202808e-02
## S.T.archiv S.T.archiv -7.202808e-02
## H.P.fashion.week H.P.fashion.week -7.632046e-02
## H.P.year.colon H.P.year.colon -7.842875e-02
## H.T.fashion H.T.fashion -7.946462e-02
## H.npnct15.log H.npnct15.log -8.273237e-02
## A.T.fashion A.T.fashion -8.416793e-02
## S.T.fashion S.T.fashion -8.417159e-02
## A.T.week A.T.week -8.542792e-02
## S.T.week S.T.week -8.552704e-02
## .clusterid .clusterid -8.644104e-02
## .clusterid.fctr .clusterid.fctr -8.644104e-02
## H.nstopwrds.log H.nstopwrds.log -8.657067e-02
## H.npnct28.log H.npnct28.log -8.917338e-02
## S.npnct11.log S.npnct11.log -9.158156e-02
## A.npnct11.log A.npnct11.log -9.183870e-02
## S.nstopwrds.log S.nstopwrds.log -1.138992e-01
## A.nstopwrds.log A.nstopwrds.log -1.144762e-01
## H.ndgts.log H.ndgts.log -1.196633e-01
## S.ndgts.log S.ndgts.log -1.239225e-01
## A.ndgts.log A.ndgts.log -1.246678e-01
## H.nuppr.log H.nuppr.log -1.278085e-01
## H.nwrds.log H.nwrds.log -1.573431e-01
## H.nchrs.log H.nchrs.log -1.710624e-01
## S.nwrds.log S.nwrds.log -1.893755e-01
## A.nwrds.log A.nwrds.log -1.895162e-01
## H.nwrds.unq.log H.nwrds.unq.log -2.013666e-01
## S.nchrs.log S.nchrs.log -2.054030e-01
## A.nchrs.log A.nchrs.log -2.054874e-01
## A.nwrds.unq.log A.nwrds.unq.log -2.372695e-01
## S.nwrds.unq.log S.nwrds.unq.log -2.373282e-01
## S.nuppr.log S.nuppr.log -2.682841e-01
## A.nuppr.log A.nuppr.log -2.685472e-01
## A.npnct05.log A.npnct05.log NA
## A.npnct09.log A.npnct09.log NA
## A.npnct22.log A.npnct22.log NA
## A.npnct26.log A.npnct26.log NA
## A.npnct27.log A.npnct27.log NA
## A.npnct29.log A.npnct29.log NA
## A.npnct30.log A.npnct30.log NA
## H.npnct09.log H.npnct09.log NA
## H.npnct17.log H.npnct17.log NA
## H.npnct18.log H.npnct18.log NA
## H.npnct21.log H.npnct21.log NA
## H.npnct22.log H.npnct22.log NA
## H.npnct23.log H.npnct23.log NA
## H.npnct25.log H.npnct25.log NA
## H.npnct26.log H.npnct26.log NA
## H.npnct27.log H.npnct27.log NA
## H.npnct29.log H.npnct29.log NA
## H.npnct30.log H.npnct30.log NA
## H.P.http H.P.http NA
## PubDate.year.fctr PubDate.year.fctr NA
## S.npnct05.log S.npnct05.log NA
## S.npnct09.log S.npnct09.log NA
## S.npnct17.log S.npnct17.log NA
## S.npnct18.log S.npnct18.log NA
## S.npnct22.log S.npnct22.log NA
## S.npnct25.log S.npnct25.log NA
## S.npnct26.log S.npnct26.log NA
## S.npnct27.log S.npnct27.log NA
## S.npnct29.log S.npnct29.log NA
## S.npnct30.log S.npnct30.log NA
## S.P.http S.P.http NA
## exclude.as.feat cor.y.abs
## Popular 1 1.000000e+00
## WordCount.log 1 2.675605e-01
## A.ratio.sum.TfIdf.nwrds 0 2.653142e-01
## WordCount.log.nonNA 0 2.652413e-01
## S.ratio.sum.TfIdf.nwrds 0 2.651816e-01
## WordCount 1 2.575265e-01
## H.ratio.sum.TfIdf.nwrds 0 2.254479e-01
## myCategory.fctr 1 1.703175e-01
## myCategory.fctr.nonNA 0 1.703175e-01
## PubDate.day.minutes.poly.1 0 1.567535e-01
## H.sum.TfIdf 0 1.519219e-01
## S.sum.TfIdf 0 1.469053e-01
## A.sum.TfIdf 0 1.462756e-01
## PubDate.hour.fctr 1 1.354368e-01
## PubDate.hour.fctr.nonNA 0 1.354368e-01
## H.npnct19.log 0 1.283641e-01
## A.ratio.nstopwrds.nwrds 0 1.112093e-01
## S.ratio.nstopwrds.nwrds 0 1.105792e-01
## PubDate.wkend 0 1.067288e-01
## H.P.recap.colon 1 9.008096e-02
## H.P.quandary 1 8.734922e-02
## PubDate.day.minutes.poly.4 0 7.394139e-02
## PubDate.day.minutes.poly.2 0 7.097772e-02
## H.P.no.comment.colon 1 6.074669e-02
## S.npnct19.log 0 5.552543e-02
## A.npnct19.log 0 5.531065e-02
## H.P.facts.figures 1 5.410097e-02
## PubDate.last10 1 5.398093e-02
## H.npnct08.log 0 5.375262e-02
## PubDate.last10.log 0 4.931702e-02
## PubDate.last1.log 0 4.635751e-02
## H.P.readers.respond 1 4.432886e-02
## A.T.make 1 4.124187e-02
## S.T.make 1 4.118050e-02
## H.ratio.nstopwrds.nwrds 0 4.024406e-02
## PubDate.last100 1 3.989229e-02
## PubDate.last1 1 3.592267e-02
## H.T.get 1 3.300192e-02
## H.npnct06.log 0 3.190718e-02
## A.npnct01.log 0 3.093101e-02
## S.npnct01.log 0 3.093101e-02
## A.T.can 1 3.083389e-02
## H.npnct16.log 0 3.039622e-02
## S.T.can 1 3.005998e-02
## PubDate.day.minutes.poly.3 0 2.798355e-02
## S.npnct21.log 0 2.760321e-02
## S.npnct23.log 0 2.760321e-02
## H.T.ebola 1 2.682920e-02
## H.npnct01.log 0 2.271577e-02
## PubDate.month.fctr 1 1.914874e-02
## A.T.said 1 1.876762e-02
## S.T.said 1 1.863436e-02
## A.npnct24.log 0 1.665130e-02
## S.npnct24.log 0 1.665130e-02
## PubDate.POSIX 1 1.568326e-02
## PubDate.zoo 1 1.568326e-02
## A.npnct21.log 0 1.537569e-02
## A.npnct23.log 0 1.537569e-02
## H.T.make 1 1.349595e-02
## H.npnct11.log 0 1.333613e-02
## UniqueID 1 1.182492e-02
## A.T.one 1 1.051414e-02
## S.T.one 1 1.050293e-02
## H.npnct03.log 0 9.533020e-03
## H.P.s.notebook 1 7.755542e-03
## H.npnct24.log 0 9.890046e-19
## H.T.take 1 1.263270e-03
## A.npnct16.log 0 1.587454e-03
## S.npnct16.log 0 1.587454e-03
## A.T.presid 1 2.090565e-03
## S.T.presid 1 2.381159e-03
## S.npnct08.log 0 3.372706e-03
## A.npnct08.log 0 4.193476e-03
## A.npnct25.log 0 5.547032e-03
## A.npnct10.log 0 5.547032e-03
## H.npnct10.log 0 5.547032e-03
## H.npnct20.log 0 5.547032e-03
## S.npnct02.log 0 5.547032e-03
## S.npnct10.log 0 5.547032e-03
## PubDate.last100.log 0 7.663322e-03
## .rnorm 0 8.244230e-03
## H.npnct05.log 0 9.653967e-03
## H.P.friday.night.music 1 9.653967e-03
## H.T.obama 1 9.682517e-03
## H.T.say 1 9.763205e-03
## H.T.bank 1 1.037439e-02
## A.npnct07.log 0 1.083506e-02
## S.npnct07.log 0 1.083506e-02
## PubDate.date.fctr 1 1.164756e-02
## PubDate.date.fctr.nonNA 0 1.164756e-02
## PubDate.second.fctr 1 1.187946e-02
## PubDate.second.fctr.nonNA 0 1.187946e-02
## H.npnct07.log 0 1.201741e-02
## S.npnct03.log 0 1.240734e-02
## A.npnct18.log 0 1.271661e-02
## A.P.http 1 1.294748e-02
## H.npnct12.log 0 1.305305e-02
## A.npnct03.log 0 1.359260e-02
## H.T.word 1 1.382927e-02
## H.T.big 1 1.438162e-02
## A.npnct02.log 0 1.451467e-02
## A.npnct17.log 0 1.457558e-02
## A.P.year.colon 1 1.569779e-02
## S.P.year.colon 1 1.569779e-02
## A.P.first.draft 1 1.841153e-02
## S.P.first.draft 1 1.841153e-02
## S.T.obama 1 1.847126e-02
## A.T.obama 1 1.847812e-02
## A.npnct20.log 0 1.923169e-02
## S.npnct20.log 0 1.923169e-02
## H.npnct02.log 0 2.001851e-02
## H.T.test 1 2.117852e-02
## S.npnct14.log 0 2.121844e-02
## H.P.on.this.day 1 2.150663e-02
## S.T.take 1 2.264447e-02
## A.T.take 1 2.271897e-02
## A.npnct06.log 0 2.389145e-02
## S.npnct06.log 0 2.389145e-02
## A.npnct14.log 0 2.407715e-02
## S.T.time 1 2.416246e-02
## A.T.time 1 2.430509e-02
## H.T.newyorktim 1 2.514415e-02
## H.npnct13.log 0 2.524770e-02
## H.T.deal 1 2.552288e-02
## S.T.new 1 2.558595e-02
## A.T.new 1 2.563613e-02
## H.T.billion 1 2.776561e-02
## A.P.metropolitan.diary.colon 1 2.841404e-02
## S.P.metropolitan.diary.colon 1 2.841404e-02
## H.T.polit 1 3.058564e-02
## H.P.verbatim.colon 1 3.194363e-02
## H.T.china 1 3.283653e-02
## H.T.art 1 3.291486e-02
## PubDate.minute.fctr 1 3.407385e-02
## PubDate.minute.fctr.nonNA 0 3.407385e-02
## H.T.read 1 3.467043e-02
## S.npnct12.log 0 3.638891e-02
## H.P.today.in.politic 1 3.733661e-02
## A.T.year 1 3.741571e-02
## S.T.year 1 3.756011e-02
## A.npnct12.log 0 3.760012e-02
## H.P.what.we.are 1 3.775209e-02
## A.T.will 1 3.845737e-02
## S.T.will 1 3.850299e-02
## A.T.appear 1 3.941362e-02
## S.T.appear 1 3.941362e-02
## PubDate.wkday.fctr 1 3.980129e-02
## PubDate.wkday.fctr.nonNA 0 3.980129e-02
## H.T.pictur 1 4.003882e-02
## H.T.new 1 4.111696e-02
## A.T.senat 1 4.139980e-02
## S.T.senat 1 4.143422e-02
## S.T.show 1 4.182920e-02
## A.T.show 1 4.185292e-02
## S.T.day 1 4.225390e-02
## A.T.day 1 4.234061e-02
## H.P.today.in.smallbusiness 1 4.243051e-02
## A.T.first 1 4.288090e-02
## S.T.first 1 4.302271e-02
## H.P.first.draft 1 4.316253e-02
## S.npnct28.log 0 4.370037e-02
## A.npnct28.log 0 4.373349e-02
## A.P.daily.clip.report 1 4.388279e-02
## H.P.daily.clip.report 1 4.388279e-02
## H.T.clip 1 4.388279e-02
## S.P.daily.clip.report 1 4.388279e-02
## H.T.news 1 4.436368e-02
## H.T.first 1 4.472902e-02
## H.T.X2014 1 4.522109e-02
## A.T.newyork 1 4.686921e-02
## S.T.newyork 1 4.694998e-02
## A.T.report 1 4.773092e-02
## A.T.compani 1 4.774812e-02
## S.T.report 1 4.778378e-02
## S.T.compani 1 4.787994e-02
## A.T.word 1 4.821561e-02
## S.T.word 1 4.822452e-02
## H.T.morn 1 4.838380e-02
## A.T.editor 1 4.875454e-02
## S.T.editor 1 4.875454e-02
## H.T.busi 1 4.899819e-02
## A.T.newyorktim 1 4.984782e-02
## S.T.newyorktim 1 4.985328e-02
## A.npnct13.log 0 4.999563e-02
## A.T.share 1 5.105597e-02
## S.T.share 1 5.105597e-02
## H.npnct04.log 0 5.126277e-02
## S.npnct13.log 0 5.332519e-02
## A.T.articl 1 5.470831e-02
## S.T.articl 1 5.471737e-02
## H.T.newyork 1 5.563086e-02
## PubDate.day.minutes.poly.5 0 5.592923e-02
## H.T.today 1 5.833786e-02
## H.T.springsumm 1 5.943248e-02
## H.T.day 1 6.044381e-02
## H.npnct14.log 0 6.158577e-02
## A.T.diari 1 6.229931e-02
## S.T.diari 1 6.229931e-02
## H.T.report 1 6.238114e-02
## A.npnct04.log 0 6.294642e-02
## S.npnct04.log 0 6.294642e-02
## H.T.daili 1 6.303731e-02
## H.T.X2015 1 6.601141e-02
## S.npnct15.log 0 6.612020e-02
## A.npnct15.log 0 6.735974e-02
## A.T.herald 1 6.752419e-02
## S.T.herald 1 6.752419e-02
## H.T.week 1 6.827601e-02
## A.T.photo 1 6.873838e-02
## S.T.photo 1 6.874283e-02
## A.T.intern 1 6.953025e-02
## S.T.intern 1 6.956906e-02
## A.T.tribun 1 7.013418e-02
## S.T.tribun 1 7.013418e-02
## A.P.fashion.week 1 7.080716e-02
## S.P.fashion.week 1 7.080716e-02
## A.T.archiv 1 7.202808e-02
## S.T.archiv 1 7.202808e-02
## H.P.fashion.week 1 7.632046e-02
## H.P.year.colon 1 7.842875e-02
## H.T.fashion 1 7.946462e-02
## H.npnct15.log 0 8.273237e-02
## A.T.fashion 1 8.416793e-02
## S.T.fashion 1 8.417159e-02
## A.T.week 1 8.542792e-02
## S.T.week 1 8.552704e-02
## .clusterid 1 8.644104e-02
## .clusterid.fctr 0 8.644104e-02
## H.nstopwrds.log 0 8.657067e-02
## H.npnct28.log 0 8.917338e-02
## S.npnct11.log 0 9.158156e-02
## A.npnct11.log 0 9.183870e-02
## S.nstopwrds.log 0 1.138992e-01
## A.nstopwrds.log 0 1.144762e-01
## H.ndgts.log 0 1.196633e-01
## S.ndgts.log 0 1.239225e-01
## A.ndgts.log 0 1.246678e-01
## H.nuppr.log 0 1.278085e-01
## H.nwrds.log 0 1.573431e-01
## H.nchrs.log 0 1.710624e-01
## S.nwrds.log 0 1.893755e-01
## A.nwrds.log 0 1.895162e-01
## H.nwrds.unq.log 0 2.013666e-01
## S.nchrs.log 0 2.054030e-01
## A.nchrs.log 0 2.054874e-01
## A.nwrds.unq.log 0 2.372695e-01
## S.nwrds.unq.log 0 2.373282e-01
## S.nuppr.log 0 2.682841e-01
## A.nuppr.log 0 2.685472e-01
## A.npnct05.log 0 NA
## A.npnct09.log 0 NA
## A.npnct22.log 0 NA
## A.npnct26.log 0 NA
## A.npnct27.log 0 NA
## A.npnct29.log 0 NA
## A.npnct30.log 0 NA
## H.npnct09.log 0 NA
## H.npnct17.log 0 NA
## H.npnct18.log 0 NA
## H.npnct21.log 0 NA
## H.npnct22.log 0 NA
## H.npnct23.log 0 NA
## H.npnct25.log 0 NA
## H.npnct26.log 0 NA
## H.npnct27.log 0 NA
## H.npnct29.log 0 NA
## H.npnct30.log 0 NA
## H.P.http 1 NA
## PubDate.year.fctr 0 NA
## S.npnct05.log 0 NA
## S.npnct09.log 0 NA
## S.npnct17.log 0 NA
## S.npnct18.log 0 NA
## S.npnct22.log 0 NA
## S.npnct25.log 0 NA
## S.npnct26.log 0 NA
## S.npnct27.log 0 NA
## S.npnct29.log 0 NA
## S.npnct30.log 0 NA
## S.P.http 1 NA
## cor.high.X freqRatio
## Popular <NA> 4.976212
## WordCount.log <NA> 1.266667
## A.ratio.sum.TfIdf.nwrds <NA> 2.583333
## WordCount.log.nonNA <NA> 1.266667
## S.ratio.sum.TfIdf.nwrds A.ratio.sum.TfIdf.nwrds 2.583333
## WordCount <NA> 2.315789
## H.ratio.sum.TfIdf.nwrds <NA> 1.148148
## myCategory.fctr <NA> 1.348739
## myCategory.fctr.nonNA <NA> 1.348739
## PubDate.day.minutes.poly.1 <NA> 1.225490
## H.sum.TfIdf <NA> 1.127273
## S.sum.TfIdf <NA> 2.583333
## A.sum.TfIdf S.sum.TfIdf 2.583333
## PubDate.hour.fctr <NA> 1.835040
## PubDate.hour.fctr.nonNA PubDate.day.minutes.poly.1 1.835040
## H.npnct19.log <NA> 14.995098
## A.ratio.nstopwrds.nwrds <NA> 1.915094
## S.ratio.nstopwrds.nwrds A.ratio.nstopwrds.nwrds 1.908517
## PubDate.wkend <NA> 9.095827
## H.P.recap.colon <NA> 93.666667
## H.P.quandary <NA> 652.200000
## PubDate.day.minutes.poly.4 <NA> 1.225490
## PubDate.day.minutes.poly.2 <NA> 1.225490
## H.P.no.comment.colon <NA> 724.777778
## S.npnct19.log <NA> 12.922246
## A.npnct19.log S.npnct19.log 12.858065
## H.P.facts.figures <NA> 1087.666667
## PubDate.last10 <NA> 1.666667
## H.npnct08.log <NA> 111.620690
## PubDate.last10.log <NA> 1.666667
## PubDate.last1.log <NA> 1.142857
## H.P.readers.respond <NA> 342.789474
## A.T.make <NA> 273.782609
## S.T.make <NA> 273.782609
## H.ratio.nstopwrds.nwrds <NA> 1.141631
## PubDate.last100 <NA> 25.000000
## PubDate.last1 <NA> 1.142857
## H.T.get <NA> 430.866667
## H.npnct06.log <NA> 68.935484
## A.npnct01.log <NA> 309.952381
## S.npnct01.log A.npnct01.log 309.952381
## A.T.can <NA> 261.666667
## H.npnct16.log H.npnct06.log 96.104478
## S.T.can <NA> 261.666667
## PubDate.day.minutes.poly.3 <NA> 1.225490
## S.npnct21.log <NA> 6531.000000
## S.npnct23.log S.npnct21.log 6531.000000
## H.T.ebola <NA> 293.000000
## H.npnct01.log <NA> 282.913043
## PubDate.month.fctr <NA> 1.017514
## A.T.said <NA> 202.516129
## S.T.said <NA> 202.516129
## A.npnct24.log <NA> 724.777778
## S.npnct24.log A.npnct24.log 724.777778
## PubDate.POSIX <NA> 1.000000
## PubDate.zoo <NA> 1.000000
## A.npnct21.log S.npnct21.log 3264.500000
## A.npnct23.log A.npnct21.log 3264.500000
## H.T.make <NA> 322.200000
## H.npnct11.log <NA> 4.937442
## UniqueID <NA> 1.000000
## A.T.one <NA> 214.931034
## S.T.one <NA> 214.965517
## H.npnct03.log <NA> 2176.333333
## H.P.s.notebook <NA> 815.500000
## H.npnct24.log <NA> 0.000000
## H.T.take <NA> 322.250000
## A.npnct16.log <NA> 434.133333
## S.npnct16.log <NA> 434.133333
## A.T.presid <NA> 232.740741
## S.T.presid <NA> 232.740741
## S.npnct08.log <NA> 175.486486
## A.npnct08.log <NA> 170.842105
## A.npnct25.log <NA> 6531.000000
## A.npnct10.log <NA> 6531.000000
## H.npnct10.log <NA> 6531.000000
## H.npnct20.log <NA> 6531.000000
## S.npnct02.log <NA> 6531.000000
## S.npnct10.log <NA> 6531.000000
## PubDate.last100.log <NA> 25.000000
## .rnorm <NA> 2.000000
## H.npnct05.log <NA> 543.333333
## H.P.friday.night.music <NA> 543.333333
## H.T.obama <NA> 221.827586
## H.T.say <NA> 247.461538
## H.T.bank <NA> 214.300000
## A.npnct07.log <NA> 2176.000000
## S.npnct07.log A.npnct07.log 2176.000000
## PubDate.date.fctr <NA> 1.021394
## PubDate.date.fctr.nonNA <NA> 1.021394
## PubDate.second.fctr <NA> 1.018204
## PubDate.second.fctr.nonNA <NA> 1.018204
## H.npnct07.log <NA> 5.437234
## S.npnct03.log A.npnct03.log 1305.400000
## A.npnct18.log A.npnct02.log 1631.500000
## A.P.http <NA> 1305.200000
## H.npnct12.log <NA> 13.126638
## A.npnct03.log <NA> 1087.666667
## H.T.word <NA> 104.096774
## H.T.big <NA> 403.562500
## A.npnct02.log A.npnct17.log 1087.500000
## A.npnct17.log A.npnct14.log 1087.500000
## A.P.year.colon <NA> 815.500000
## S.P.year.colon <NA> 815.500000
## A.P.first.draft <NA> 592.818182
## S.P.first.draft <NA> 592.818182
## S.T.obama <NA> 398.687500
## A.T.obama <NA> 398.687500
## A.npnct20.log <NA> 543.333333
## S.npnct20.log A.npnct20.log 543.333333
## H.npnct02.log <NA> 501.461538
## H.T.test <NA> 306.666667
## S.npnct14.log <NA> 203.062500
## H.P.on.this.day <NA> 434.466667
## S.T.take <NA> 274.608696
## A.T.take <NA> 274.565217
## A.npnct06.log <NA> 115.642857
## S.npnct06.log A.npnct06.log 115.642857
## A.npnct14.log <NA> 196.696970
## S.T.time <NA> 217.862069
## A.T.time <NA> 217.827586
## H.T.newyorktim <NA> 433.266667
## H.npnct13.log <NA> 22.802326
## H.T.deal <NA> 238.962963
## S.T.new <NA> 114.442308
## A.T.new <NA> 114.423077
## H.T.billion <NA> 214.533333
## A.P.metropolitan.diary.colon <NA> 99.492308
## S.P.metropolitan.diary.colon <NA> 99.492308
## H.T.polit <NA> 128.780000
## H.P.verbatim.colon <NA> 196.939394
## H.T.china <NA> 238.407407
## H.T.art <NA> 293.363636
## PubDate.minute.fctr <NA> 1.483365
## PubDate.minute.fctr.nonNA <NA> 1.483365
## H.T.read <NA> 179.388889
## S.npnct12.log A.npnct12.log 5.706263
## H.P.today.in.politic <NA> 144.155556
## A.T.year <NA> 160.815789
## S.T.year <NA> 160.815789
## A.npnct12.log <NA> 5.715368
## H.P.what.we.are <NA> 141.000000
## A.T.will <NA> 121.755102
## S.T.will <NA> 119.360000
## A.T.appear <NA> 228.821429
## S.T.appear <NA> 228.821429
## PubDate.wkday.fctr <NA> 1.003268
## PubDate.wkday.fctr.nonNA <NA> 1.003268
## H.T.pictur <NA> 99.230769
## H.T.new <NA> 123.333333
## A.T.senat <NA> 372.294118
## S.T.senat <NA> 372.352941
## S.T.show <NA> 274.608696
## A.T.show <NA> 263.166667
## S.T.day <NA> 89.542857
## A.T.day <NA> 89.528571
## H.P.today.in.smallbusiness <NA> 111.620690
## A.T.first <NA> 225.392857
## S.T.first <NA> 225.392857
## H.P.first.draft <NA> 107.866667
## S.npnct28.log A.npnct28.log 134.791667
## A.npnct28.log <NA> 126.862745
## A.P.daily.clip.report <NA> 104.354839
## H.P.daily.clip.report <NA> 104.354839
## H.T.clip <NA> 104.354839
## S.P.daily.clip.report <NA> 104.354839
## H.T.news <NA> 322.000000
## H.T.first <NA> 194.727273
## H.T.X2014 <NA> 110.879310
## A.T.newyork <NA> 149.547619
## S.T.newyork <NA> 149.547619
## A.T.report <NA> 80.371795
## A.T.compani <NA> 137.111111
## S.T.report <NA> 80.371795
## S.T.compani <NA> 137.111111
## A.T.word <NA> 133.125000
## S.T.word <NA> 133.125000
## H.T.morn <NA> 165.205128
## A.T.editor <NA> 228.892857
## S.T.editor <NA> 228.892857
## H.T.busi <NA> 229.428571
## A.T.newyorktim <NA> 84.540541
## S.T.newyorktim <NA> 84.540541
## A.npnct13.log S.npnct13.log 4.603330
## A.T.share <NA> 234.629630
## S.T.share <NA> 234.629630
## H.npnct04.log <NA> 38.325301
## S.npnct13.log <NA> 4.672000
## A.T.articl <NA> 85.500000
## S.T.articl <NA> 85.500000
## H.T.newyork <NA> 114.563636
## PubDate.day.minutes.poly.5 <NA> 1.225490
## H.T.today <NA> 138.239130
## H.T.springsumm <NA> 106.966667
## H.T.day <NA> 86.547945
## H.npnct14.log <NA> 52.983471
## A.T.diari <NA> 71.528090
## S.T.diari <NA> 71.528090
## H.T.report <NA> 102.000000
## A.npnct04.log <NA> 28.536364
## S.npnct04.log A.npnct04.log 28.536364
## H.T.daili <NA> 102.903226
## H.T.X2015 <NA> 96.833333
## S.npnct15.log A.npnct15.log 13.915332
## A.npnct15.log <NA> 13.744344
## A.T.herald <NA> 144.750000
## S.T.herald <NA> 144.750000
## H.T.week <NA> 71.352273
## A.T.photo <NA> 70.400000
## S.T.photo <NA> 70.400000
## A.T.intern <NA> 140.400000
## S.T.intern <NA> 140.400000
## A.T.tribun <NA> 144.750000
## S.T.tribun <NA> 144.750000
## A.P.fashion.week <NA> 40.081761
## S.P.fashion.week <NA> 40.081761
## A.T.archiv <NA> 144.545455
## S.T.archiv <NA> 144.545455
## H.P.fashion.week <NA> 34.500000
## H.P.year.colon <NA> 32.670103
## H.T.fashion <NA> 77.876543
## H.npnct15.log <NA> 3.914910
## A.T.fashion <NA> 59.245283
## S.T.fashion <NA> 59.245283
## A.T.week <NA> 56.560748
## S.T.week <NA> 56.560748
## .clusterid <NA> 7.504202
## .clusterid.fctr <NA> 7.504202
## H.nstopwrds.log <NA> 1.370729
## H.npnct28.log <NA> 24.123077
## S.npnct11.log A.npnct11.log 1.660473
## A.npnct11.log <NA> 1.660473
## S.nstopwrds.log A.nstopwrds.log 1.097879
## A.nstopwrds.log <NA> 1.096091
## H.ndgts.log <NA> 13.616137
## S.ndgts.log A.ndgts.log 10.558522
## A.ndgts.log <NA> 10.548255
## H.nuppr.log H.nwrds.unq.log 1.033930
## H.nwrds.log H.nchrs.log 1.104308
## H.nchrs.log H.nwrds.unq.log 1.023810
## S.nwrds.log A.nwrds.log 1.049342
## A.nwrds.log A.nchrs.log 1.052805
## H.nwrds.unq.log <NA> 1.000814
## S.nchrs.log A.nchrs.log 1.328571
## A.nchrs.log S.nwrds.unq.log 1.328571
## A.nwrds.unq.log S.nwrds.unq.log 1.052239
## S.nwrds.unq.log A.ratio.sum.TfIdf.nwrds 1.052239
## S.nuppr.log A.nuppr.log 1.155251
## A.nuppr.log <NA> 1.153934
## A.npnct05.log <NA> 0.000000
## A.npnct09.log <NA> 0.000000
## A.npnct22.log <NA> 0.000000
## A.npnct26.log <NA> 0.000000
## A.npnct27.log <NA> 0.000000
## A.npnct29.log <NA> 0.000000
## A.npnct30.log <NA> 0.000000
## H.npnct09.log <NA> 0.000000
## H.npnct17.log <NA> 0.000000
## H.npnct18.log <NA> 0.000000
## H.npnct21.log <NA> 0.000000
## H.npnct22.log <NA> 0.000000
## H.npnct23.log <NA> 0.000000
## H.npnct25.log <NA> 0.000000
## H.npnct26.log <NA> 0.000000
## H.npnct27.log <NA> 0.000000
## H.npnct29.log <NA> 0.000000
## H.npnct30.log <NA> 0.000000
## H.P.http <NA> 0.000000
## PubDate.year.fctr <NA> 0.000000
## S.npnct05.log <NA> 0.000000
## S.npnct09.log <NA> 0.000000
## S.npnct17.log <NA> 0.000000
## S.npnct18.log <NA> 0.000000
## S.npnct22.log <NA> 0.000000
## S.npnct25.log <NA> 0.000000
## S.npnct26.log <NA> 0.000000
## S.npnct27.log <NA> 0.000000
## S.npnct29.log <NA> 0.000000
## S.npnct30.log <NA> 0.000000
## S.P.http <NA> 0.000000
## percentUnique zeroVar nzv myNearZV
## Popular 0.03061849 FALSE FALSE FALSE
## WordCount.log 24.14268218 FALSE FALSE FALSE
## A.ratio.sum.TfIdf.nwrds 94.39681568 FALSE FALSE FALSE
## WordCount.log.nonNA 24.18860992 FALSE FALSE FALSE
## S.ratio.sum.TfIdf.nwrds 94.35088794 FALSE FALSE FALSE
## WordCount 24.15799143 FALSE FALSE FALSE
## H.ratio.sum.TfIdf.nwrds 90.47764850 FALSE FALSE FALSE
## myCategory.fctr 0.30618494 FALSE FALSE FALSE
## myCategory.fctr.nonNA 0.30618494 FALSE FALSE FALSE
## PubDate.day.minutes.poly.1 18.08022045 FALSE FALSE FALSE
## H.sum.TfIdf 84.47642376 FALSE FALSE FALSE
## S.sum.TfIdf 94.16717697 FALSE FALSE FALSE
## A.sum.TfIdf 94.13655848 FALSE FALSE FALSE
## PubDate.hour.fctr 0.04592774 FALSE FALSE FALSE
## PubDate.hour.fctr.nonNA 0.04592774 FALSE FALSE FALSE
## H.npnct19.log 0.06123699 FALSE FALSE FALSE
## A.ratio.nstopwrds.nwrds 4.11818739 FALSE FALSE FALSE
## S.ratio.nstopwrds.nwrds 3.76607471 FALSE FALSE FALSE
## PubDate.wkend 0.03061849 FALSE FALSE FALSE
## H.P.recap.colon 0.03061849 FALSE TRUE FALSE
## H.P.quandary 0.03061849 FALSE TRUE FALSE
## PubDate.day.minutes.poly.4 18.08022045 FALSE FALSE FALSE
## PubDate.day.minutes.poly.2 18.08022045 FALSE FALSE FALSE
## H.P.no.comment.colon 0.03061849 FALSE TRUE FALSE
## S.npnct19.log 0.07654623 FALSE FALSE FALSE
## A.npnct19.log 0.07654623 FALSE FALSE FALSE
## H.P.facts.figures 0.03061849 FALSE TRUE FALSE
## PubDate.last10 79.05695040 FALSE FALSE FALSE
## H.npnct08.log 0.03061849 FALSE TRUE FALSE
## PubDate.last10.log 79.05695040 FALSE FALSE FALSE
## PubDate.last1.log 36.49724434 FALSE FALSE FALSE
## H.P.readers.respond 0.03061849 FALSE TRUE FALSE
## A.T.make 0.44396816 FALSE TRUE FALSE
## S.T.make 0.44396816 FALSE TRUE FALSE
## H.ratio.nstopwrds.nwrds 0.96448255 FALSE FALSE FALSE
## PubDate.last100 92.52908757 FALSE FALSE FALSE
## PubDate.last1 36.49724434 FALSE FALSE FALSE
## H.T.get 0.18371096 FALSE TRUE FALSE
## H.npnct06.log 0.06123699 FALSE TRUE FALSE
## A.npnct01.log 0.06123699 FALSE TRUE FALSE
## S.npnct01.log 0.06123699 FALSE TRUE FALSE
## A.T.can 0.48989590 FALSE TRUE FALSE
## H.npnct16.log 0.06123699 FALSE TRUE FALSE
## S.T.can 0.41334966 FALSE TRUE FALSE
## PubDate.day.minutes.poly.3 18.08022045 FALSE FALSE FALSE
## S.npnct21.log 0.03061849 FALSE TRUE TRUE
## S.npnct23.log 0.03061849 FALSE TRUE TRUE
## H.T.ebola 0.16840171 FALSE TRUE FALSE
## H.npnct01.log 0.04592774 FALSE TRUE FALSE
## PubDate.month.fctr 0.04592774 FALSE FALSE FALSE
## A.T.said 0.41334966 FALSE TRUE FALSE
## S.T.said 0.38273117 FALSE TRUE FALSE
## A.npnct24.log 0.03061849 FALSE TRUE FALSE
## S.npnct24.log 0.03061849 FALSE TRUE FALSE
## PubDate.POSIX 99.86221678 FALSE FALSE FALSE
## PubDate.zoo 99.86221678 FALSE FALSE FALSE
## A.npnct21.log 0.04592774 FALSE TRUE TRUE
## A.npnct23.log 0.04592774 FALSE TRUE TRUE
## H.T.make 0.13778322 FALSE TRUE FALSE
## H.npnct11.log 0.07654623 FALSE FALSE FALSE
## UniqueID 100.00000000 FALSE FALSE FALSE
## A.T.one 0.48989590 FALSE TRUE FALSE
## S.T.one 0.44396816 FALSE TRUE FALSE
## H.npnct03.log 0.03061849 FALSE TRUE TRUE
## H.P.s.notebook 0.03061849 FALSE TRUE FALSE
## H.npnct24.log 0.01530925 TRUE TRUE TRUE
## H.T.take 0.15309247 FALSE TRUE FALSE
## A.npnct16.log 0.04592774 FALSE TRUE FALSE
## S.npnct16.log 0.04592774 FALSE TRUE FALSE
## A.T.presid 0.45927740 FALSE TRUE FALSE
## S.T.presid 0.42865891 FALSE TRUE FALSE
## S.npnct08.log 0.06123699 FALSE TRUE FALSE
## A.npnct08.log 0.06123699 FALSE TRUE FALSE
## A.npnct25.log 0.03061849 FALSE TRUE TRUE
## A.npnct10.log 0.03061849 FALSE TRUE TRUE
## H.npnct10.log 0.03061849 FALSE TRUE TRUE
## H.npnct20.log 0.03061849 FALSE TRUE TRUE
## S.npnct02.log 0.03061849 FALSE TRUE TRUE
## S.npnct10.log 0.03061849 FALSE TRUE TRUE
## PubDate.last100.log 92.19228414 FALSE FALSE FALSE
## .rnorm 99.98469075 FALSE FALSE FALSE
## H.npnct05.log 0.03061849 FALSE TRUE FALSE
## H.P.friday.night.music 0.03061849 FALSE TRUE FALSE
## H.T.obama 0.16840171 FALSE TRUE FALSE
## H.T.say 0.16840171 FALSE TRUE FALSE
## H.T.bank 0.13778322 FALSE TRUE FALSE
## A.npnct07.log 0.04592774 FALSE TRUE TRUE
## S.npnct07.log 0.04592774 FALSE TRUE TRUE
## PubDate.date.fctr 0.07654623 FALSE FALSE FALSE
## PubDate.date.fctr.nonNA 0.07654623 FALSE FALSE FALSE
## PubDate.second.fctr 0.06123699 FALSE FALSE FALSE
## PubDate.second.fctr.nonNA 0.06123699 FALSE FALSE FALSE
## H.npnct07.log 0.12247397 FALSE FALSE FALSE
## S.npnct03.log 0.03061849 FALSE TRUE FALSE
## A.npnct18.log 0.06123699 FALSE TRUE FALSE
## A.P.http 0.04592774 FALSE TRUE FALSE
## H.npnct12.log 0.09185548 FALSE FALSE FALSE
## A.npnct03.log 0.03061849 FALSE TRUE FALSE
## H.T.word 0.13778322 FALSE TRUE FALSE
## H.T.big 0.19902021 FALSE TRUE FALSE
## A.npnct02.log 0.04592774 FALSE TRUE FALSE
## A.npnct17.log 0.04592774 FALSE TRUE FALSE
## A.P.year.colon 0.03061849 FALSE TRUE FALSE
## S.P.year.colon 0.03061849 FALSE TRUE FALSE
## A.P.first.draft 0.03061849 FALSE TRUE FALSE
## S.P.first.draft 0.03061849 FALSE TRUE FALSE
## S.T.obama 0.38273117 FALSE TRUE FALSE
## A.T.obama 0.42865891 FALSE TRUE FALSE
## A.npnct20.log 0.03061849 FALSE TRUE FALSE
## S.npnct20.log 0.03061849 FALSE TRUE FALSE
## H.npnct02.log 0.03061849 FALSE TRUE FALSE
## H.T.test 0.13778322 FALSE TRUE FALSE
## S.npnct14.log 0.04592774 FALSE TRUE FALSE
## H.P.on.this.day 0.03061849 FALSE TRUE FALSE
## S.T.take 0.38273117 FALSE TRUE FALSE
## A.T.take 0.42865891 FALSE TRUE FALSE
## A.npnct06.log 0.03061849 FALSE TRUE FALSE
## S.npnct06.log 0.03061849 FALSE TRUE FALSE
## A.npnct14.log 0.10716473 FALSE TRUE FALSE
## S.T.time 0.42865891 FALSE TRUE FALSE
## A.T.time 0.42865891 FALSE TRUE FALSE
## H.T.newyorktim 0.12247397 FALSE TRUE FALSE
## H.npnct13.log 0.12247397 FALSE TRUE FALSE
## H.T.deal 0.13778322 FALSE TRUE FALSE
## S.T.new 0.47458665 FALSE TRUE FALSE
## A.T.new 0.48989590 FALSE TRUE FALSE
## H.T.billion 0.13778322 FALSE TRUE FALSE
## A.P.metropolitan.diary.colon 0.03061849 FALSE TRUE FALSE
## S.P.metropolitan.diary.colon 0.03061849 FALSE TRUE FALSE
## H.T.polit 0.13778322 FALSE TRUE FALSE
## H.P.verbatim.colon 0.03061849 FALSE TRUE FALSE
## H.T.china 0.16840171 FALSE TRUE FALSE
## H.T.art 0.19902021 FALSE TRUE FALSE
## PubDate.minute.fctr 0.06123699 FALSE FALSE FALSE
## PubDate.minute.fctr.nonNA 0.06123699 FALSE FALSE FALSE
## H.T.read 0.16840171 FALSE TRUE FALSE
## S.npnct12.log 0.09185548 FALSE FALSE FALSE
## H.P.today.in.politic 0.03061849 FALSE TRUE FALSE
## A.T.year 0.48989590 FALSE TRUE FALSE
## S.T.year 0.45927740 FALSE TRUE FALSE
## A.npnct12.log 0.12247397 FALSE FALSE FALSE
## H.P.what.we.are 0.03061849 FALSE TRUE FALSE
## A.T.will 0.59706062 FALSE TRUE FALSE
## S.T.will 0.55113288 FALSE TRUE FALSE
## A.T.appear 0.30618494 FALSE TRUE FALSE
## S.T.appear 0.30618494 FALSE TRUE FALSE
## PubDate.wkday.fctr 0.10716473 FALSE FALSE FALSE
## PubDate.wkday.fctr.nonNA 0.10716473 FALSE FALSE FALSE
## H.T.pictur 0.10716473 FALSE TRUE FALSE
## H.T.new 0.19902021 FALSE TRUE FALSE
## A.T.senat 0.50520514 FALSE TRUE FALSE
## S.T.senat 0.47458665 FALSE TRUE FALSE
## S.T.show 0.38273117 FALSE TRUE FALSE
## A.T.show 0.39804042 FALSE TRUE FALSE
## S.T.day 0.41334966 FALSE TRUE FALSE
## A.T.day 0.44396816 FALSE TRUE FALSE
## H.P.today.in.smallbusiness 0.03061849 FALSE TRUE FALSE
## A.T.first 0.41334966 FALSE TRUE FALSE
## S.T.first 0.39804042 FALSE TRUE FALSE
## H.P.first.draft 0.03061849 FALSE TRUE FALSE
## S.npnct28.log 0.04592774 FALSE TRUE FALSE
## A.npnct28.log 0.04592774 FALSE TRUE FALSE
## A.P.daily.clip.report 0.03061849 FALSE TRUE FALSE
## H.P.daily.clip.report 0.03061849 FALSE TRUE FALSE
## H.T.clip 0.03061849 FALSE TRUE FALSE
## S.P.daily.clip.report 0.03061849 FALSE TRUE FALSE
## H.T.news 0.15309247 FALSE TRUE FALSE
## H.T.first 0.15309247 FALSE TRUE FALSE
## H.T.X2014 0.13778322 FALSE TRUE FALSE
## A.T.newyork 0.44396816 FALSE TRUE FALSE
## S.T.newyork 0.41334966 FALSE TRUE FALSE
## A.T.report 0.38273117 FALSE TRUE FALSE
## A.T.compani 0.48989590 FALSE TRUE FALSE
## S.T.report 0.35211268 FALSE TRUE FALSE
## S.T.compani 0.44396816 FALSE TRUE FALSE
## A.T.word 0.30618494 FALSE TRUE FALSE
## S.T.word 0.30618494 FALSE TRUE FALSE
## H.T.morn 0.07654623 FALSE TRUE FALSE
## A.T.editor 0.27556644 FALSE TRUE FALSE
## S.T.editor 0.27556644 FALSE TRUE FALSE
## H.T.busi 0.18371096 FALSE TRUE FALSE
## A.T.newyorktim 0.32149418 FALSE TRUE FALSE
## S.T.newyorktim 0.33680343 FALSE TRUE FALSE
## A.npnct13.log 0.16840171 FALSE FALSE FALSE
## A.T.share 0.38273117 FALSE TRUE FALSE
## S.T.share 0.38273117 FALSE TRUE FALSE
## H.npnct04.log 0.04592774 FALSE TRUE FALSE
## S.npnct13.log 0.16840171 FALSE FALSE FALSE
## A.T.articl 0.29087569 FALSE TRUE FALSE
## S.T.articl 0.29087569 FALSE TRUE FALSE
## H.T.newyork 0.15309247 FALSE TRUE FALSE
## PubDate.day.minutes.poly.5 18.08022045 FALSE FALSE FALSE
## H.T.today 0.13778322 FALSE TRUE FALSE
## H.T.springsumm 0.09185548 FALSE TRUE FALSE
## H.T.day 0.18371096 FALSE TRUE FALSE
## H.npnct14.log 0.03061849 FALSE TRUE FALSE
## A.T.diari 0.18371096 FALSE TRUE FALSE
## S.T.diari 0.18371096 FALSE TRUE FALSE
## H.T.report 0.16840171 FALSE TRUE FALSE
## A.npnct04.log 0.07654623 FALSE TRUE FALSE
## S.npnct04.log 0.07654623 FALSE TRUE FALSE
## H.T.daili 0.16840171 FALSE TRUE FALSE
## H.T.X2015 0.10716473 FALSE TRUE FALSE
## S.npnct15.log 0.04592774 FALSE FALSE FALSE
## A.npnct15.log 0.04592774 FALSE FALSE FALSE
## A.T.herald 0.24494795 FALSE TRUE FALSE
## S.T.herald 0.24494795 FALSE TRUE FALSE
## H.T.week 0.16840171 FALSE TRUE FALSE
## A.T.photo 0.27556644 FALSE TRUE FALSE
## S.T.photo 0.29087569 FALSE TRUE FALSE
## A.T.intern 0.32149418 FALSE TRUE FALSE
## S.T.intern 0.30618494 FALSE TRUE FALSE
## A.T.tribun 0.24494795 FALSE TRUE FALSE
## S.T.tribun 0.24494795 FALSE TRUE FALSE
## A.P.fashion.week 0.03061849 FALSE TRUE FALSE
## S.P.fashion.week 0.03061849 FALSE TRUE FALSE
## A.T.archiv 0.24494795 FALSE TRUE FALSE
## S.T.archiv 0.24494795 FALSE TRUE FALSE
## H.P.fashion.week 0.03061849 FALSE TRUE FALSE
## H.P.year.colon 0.03061849 FALSE TRUE FALSE
## H.T.fashion 0.19902021 FALSE TRUE FALSE
## H.npnct15.log 0.04592774 FALSE FALSE FALSE
## A.T.fashion 0.39804042 FALSE TRUE FALSE
## S.T.fashion 0.38273117 FALSE TRUE FALSE
## A.T.week 0.47458665 FALSE TRUE FALSE
## S.T.week 0.41334966 FALSE TRUE FALSE
## .clusterid 0.26025720 FALSE FALSE FALSE
## .clusterid.fctr 0.26025720 FALSE FALSE FALSE
## H.nstopwrds.log 0.12247397 FALSE FALSE FALSE
## H.npnct28.log 0.03061849 FALSE TRUE FALSE
## S.npnct11.log 0.13778322 FALSE FALSE FALSE
## A.npnct11.log 0.13778322 FALSE FALSE FALSE
## S.nstopwrds.log 0.38273117 FALSE FALSE FALSE
## A.nstopwrds.log 0.42865891 FALSE FALSE FALSE
## H.ndgts.log 0.18371096 FALSE FALSE FALSE
## S.ndgts.log 0.26025720 FALSE FALSE FALSE
## A.ndgts.log 0.29087569 FALSE FALSE FALSE
## H.nuppr.log 0.29087569 FALSE FALSE FALSE
## H.nwrds.log 0.32149418 FALSE FALSE FALSE
## H.nchrs.log 1.57685242 FALSE FALSE FALSE
## S.nwrds.log 0.75015309 FALSE FALSE FALSE
## A.nwrds.log 0.94917330 FALSE FALSE FALSE
## H.nwrds.unq.log 0.21432945 FALSE FALSE FALSE
## S.nchrs.log 3.73545622 FALSE FALSE FALSE
## A.nchrs.log 4.40906307 FALSE FALSE FALSE
## A.nwrds.unq.log 0.55113288 FALSE FALSE FALSE
## S.nwrds.unq.log 0.44396816 FALSE FALSE FALSE
## S.nuppr.log 0.33680343 FALSE FALSE FALSE
## A.nuppr.log 0.33680343 FALSE FALSE FALSE
## A.npnct05.log 0.01530925 TRUE TRUE TRUE
## A.npnct09.log 0.01530925 TRUE TRUE TRUE
## A.npnct22.log 0.01530925 TRUE TRUE TRUE
## A.npnct26.log 0.01530925 TRUE TRUE TRUE
## A.npnct27.log 0.01530925 TRUE TRUE TRUE
## A.npnct29.log 0.01530925 TRUE TRUE TRUE
## A.npnct30.log 0.01530925 TRUE TRUE TRUE
## H.npnct09.log 0.01530925 TRUE TRUE TRUE
## H.npnct17.log 0.01530925 TRUE TRUE TRUE
## H.npnct18.log 0.01530925 TRUE TRUE TRUE
## H.npnct21.log 0.01530925 TRUE TRUE TRUE
## H.npnct22.log 0.01530925 TRUE TRUE TRUE
## H.npnct23.log 0.01530925 TRUE TRUE TRUE
## H.npnct25.log 0.01530925 TRUE TRUE TRUE
## H.npnct26.log 0.01530925 TRUE TRUE TRUE
## H.npnct27.log 0.01530925 TRUE TRUE TRUE
## H.npnct29.log 0.01530925 TRUE TRUE TRUE
## H.npnct30.log 0.01530925 TRUE TRUE TRUE
## H.P.http 0.01530925 TRUE TRUE TRUE
## PubDate.year.fctr 0.01530925 TRUE TRUE TRUE
## S.npnct05.log 0.01530925 TRUE TRUE TRUE
## S.npnct09.log 0.01530925 TRUE TRUE TRUE
## S.npnct17.log 0.01530925 TRUE TRUE TRUE
## S.npnct18.log 0.01530925 TRUE TRUE TRUE
## S.npnct22.log 0.01530925 TRUE TRUE TRUE
## S.npnct25.log 0.01530925 TRUE TRUE TRUE
## S.npnct26.log 0.01530925 TRUE TRUE TRUE
## S.npnct27.log 0.01530925 TRUE TRUE TRUE
## S.npnct29.log 0.01530925 TRUE TRUE TRUE
## S.npnct30.log 0.01530925 TRUE TRUE TRUE
## S.P.http 0.01530925 TRUE TRUE TRUE
## is.cor.y.abs.low
## Popular FALSE
## WordCount.log FALSE
## A.ratio.sum.TfIdf.nwrds FALSE
## WordCount.log.nonNA FALSE
## S.ratio.sum.TfIdf.nwrds FALSE
## WordCount FALSE
## H.ratio.sum.TfIdf.nwrds FALSE
## myCategory.fctr FALSE
## myCategory.fctr.nonNA FALSE
## PubDate.day.minutes.poly.1 FALSE
## H.sum.TfIdf FALSE
## S.sum.TfIdf FALSE
## A.sum.TfIdf FALSE
## PubDate.hour.fctr FALSE
## PubDate.hour.fctr.nonNA FALSE
## H.npnct19.log FALSE
## A.ratio.nstopwrds.nwrds FALSE
## S.ratio.nstopwrds.nwrds FALSE
## PubDate.wkend FALSE
## H.P.recap.colon FALSE
## H.P.quandary FALSE
## PubDate.day.minutes.poly.4 FALSE
## PubDate.day.minutes.poly.2 FALSE
## H.P.no.comment.colon FALSE
## S.npnct19.log FALSE
## A.npnct19.log FALSE
## H.P.facts.figures FALSE
## PubDate.last10 FALSE
## H.npnct08.log FALSE
## PubDate.last10.log FALSE
## PubDate.last1.log FALSE
## H.P.readers.respond FALSE
## A.T.make FALSE
## S.T.make FALSE
## H.ratio.nstopwrds.nwrds FALSE
## PubDate.last100 FALSE
## PubDate.last1 FALSE
## H.T.get FALSE
## H.npnct06.log FALSE
## A.npnct01.log FALSE
## S.npnct01.log FALSE
## A.T.can FALSE
## H.npnct16.log FALSE
## S.T.can FALSE
## PubDate.day.minutes.poly.3 FALSE
## S.npnct21.log FALSE
## S.npnct23.log FALSE
## H.T.ebola FALSE
## H.npnct01.log FALSE
## PubDate.month.fctr FALSE
## A.T.said FALSE
## S.T.said FALSE
## A.npnct24.log FALSE
## S.npnct24.log FALSE
## PubDate.POSIX FALSE
## PubDate.zoo FALSE
## A.npnct21.log FALSE
## A.npnct23.log FALSE
## H.T.make FALSE
## H.npnct11.log FALSE
## UniqueID FALSE
## A.T.one FALSE
## S.T.one FALSE
## H.npnct03.log FALSE
## H.P.s.notebook TRUE
## H.npnct24.log TRUE
## H.T.take TRUE
## A.npnct16.log TRUE
## S.npnct16.log TRUE
## A.T.presid TRUE
## S.T.presid TRUE
## S.npnct08.log TRUE
## A.npnct08.log TRUE
## A.npnct25.log TRUE
## A.npnct10.log TRUE
## H.npnct10.log TRUE
## H.npnct20.log TRUE
## S.npnct02.log TRUE
## S.npnct10.log TRUE
## PubDate.last100.log TRUE
## .rnorm FALSE
## H.npnct05.log FALSE
## H.P.friday.night.music FALSE
## H.T.obama FALSE
## H.T.say FALSE
## H.T.bank FALSE
## A.npnct07.log FALSE
## S.npnct07.log FALSE
## PubDate.date.fctr FALSE
## PubDate.date.fctr.nonNA FALSE
## PubDate.second.fctr FALSE
## PubDate.second.fctr.nonNA FALSE
## H.npnct07.log FALSE
## S.npnct03.log FALSE
## A.npnct18.log FALSE
## A.P.http FALSE
## H.npnct12.log FALSE
## A.npnct03.log FALSE
## H.T.word FALSE
## H.T.big FALSE
## A.npnct02.log FALSE
## A.npnct17.log FALSE
## A.P.year.colon FALSE
## S.P.year.colon FALSE
## A.P.first.draft FALSE
## S.P.first.draft FALSE
## S.T.obama FALSE
## A.T.obama FALSE
## A.npnct20.log FALSE
## S.npnct20.log FALSE
## H.npnct02.log FALSE
## H.T.test FALSE
## S.npnct14.log FALSE
## H.P.on.this.day FALSE
## S.T.take FALSE
## A.T.take FALSE
## A.npnct06.log FALSE
## S.npnct06.log FALSE
## A.npnct14.log FALSE
## S.T.time FALSE
## A.T.time FALSE
## H.T.newyorktim FALSE
## H.npnct13.log FALSE
## H.T.deal FALSE
## S.T.new FALSE
## A.T.new FALSE
## H.T.billion FALSE
## A.P.metropolitan.diary.colon FALSE
## S.P.metropolitan.diary.colon FALSE
## H.T.polit FALSE
## H.P.verbatim.colon FALSE
## H.T.china FALSE
## H.T.art FALSE
## PubDate.minute.fctr FALSE
## PubDate.minute.fctr.nonNA FALSE
## H.T.read FALSE
## S.npnct12.log FALSE
## H.P.today.in.politic FALSE
## A.T.year FALSE
## S.T.year FALSE
## A.npnct12.log FALSE
## H.P.what.we.are FALSE
## A.T.will FALSE
## S.T.will FALSE
## A.T.appear FALSE
## S.T.appear FALSE
## PubDate.wkday.fctr FALSE
## PubDate.wkday.fctr.nonNA FALSE
## H.T.pictur FALSE
## H.T.new FALSE
## A.T.senat FALSE
## S.T.senat FALSE
## S.T.show FALSE
## A.T.show FALSE
## S.T.day FALSE
## A.T.day FALSE
## H.P.today.in.smallbusiness FALSE
## A.T.first FALSE
## S.T.first FALSE
## H.P.first.draft FALSE
## S.npnct28.log FALSE
## A.npnct28.log FALSE
## A.P.daily.clip.report FALSE
## H.P.daily.clip.report FALSE
## H.T.clip FALSE
## S.P.daily.clip.report FALSE
## H.T.news FALSE
## H.T.first FALSE
## H.T.X2014 FALSE
## A.T.newyork FALSE
## S.T.newyork FALSE
## A.T.report FALSE
## A.T.compani FALSE
## S.T.report FALSE
## S.T.compani FALSE
## A.T.word FALSE
## S.T.word FALSE
## H.T.morn FALSE
## A.T.editor FALSE
## S.T.editor FALSE
## H.T.busi FALSE
## A.T.newyorktim FALSE
## S.T.newyorktim FALSE
## A.npnct13.log FALSE
## A.T.share FALSE
## S.T.share FALSE
## H.npnct04.log FALSE
## S.npnct13.log FALSE
## A.T.articl FALSE
## S.T.articl FALSE
## H.T.newyork FALSE
## PubDate.day.minutes.poly.5 FALSE
## H.T.today FALSE
## H.T.springsumm FALSE
## H.T.day FALSE
## H.npnct14.log FALSE
## A.T.diari FALSE
## S.T.diari FALSE
## H.T.report FALSE
## A.npnct04.log FALSE
## S.npnct04.log FALSE
## H.T.daili FALSE
## H.T.X2015 FALSE
## S.npnct15.log FALSE
## A.npnct15.log FALSE
## A.T.herald FALSE
## S.T.herald FALSE
## H.T.week FALSE
## A.T.photo FALSE
## S.T.photo FALSE
## A.T.intern FALSE
## S.T.intern FALSE
## A.T.tribun FALSE
## S.T.tribun FALSE
## A.P.fashion.week FALSE
## S.P.fashion.week FALSE
## A.T.archiv FALSE
## S.T.archiv FALSE
## H.P.fashion.week FALSE
## H.P.year.colon FALSE
## H.T.fashion FALSE
## H.npnct15.log FALSE
## A.T.fashion FALSE
## S.T.fashion FALSE
## A.T.week FALSE
## S.T.week FALSE
## .clusterid FALSE
## .clusterid.fctr FALSE
## H.nstopwrds.log FALSE
## H.npnct28.log FALSE
## S.npnct11.log FALSE
## A.npnct11.log FALSE
## S.nstopwrds.log FALSE
## A.nstopwrds.log FALSE
## H.ndgts.log FALSE
## S.ndgts.log FALSE
## A.ndgts.log FALSE
## H.nuppr.log FALSE
## H.nwrds.log FALSE
## H.nchrs.log FALSE
## S.nwrds.log FALSE
## A.nwrds.log FALSE
## H.nwrds.unq.log FALSE
## S.nchrs.log FALSE
## A.nchrs.log FALSE
## A.nwrds.unq.log FALSE
## S.nwrds.unq.log FALSE
## S.nuppr.log FALSE
## A.nuppr.log FALSE
## A.npnct05.log NA
## A.npnct09.log NA
## A.npnct22.log NA
## A.npnct26.log NA
## A.npnct27.log NA
## A.npnct29.log NA
## A.npnct30.log NA
## H.npnct09.log NA
## H.npnct17.log NA
## H.npnct18.log NA
## H.npnct21.log NA
## H.npnct22.log NA
## H.npnct23.log NA
## H.npnct25.log NA
## H.npnct26.log NA
## H.npnct27.log NA
## H.npnct29.log NA
## H.npnct30.log NA
## H.P.http NA
## PubDate.year.fctr NA
## S.npnct05.log NA
## S.npnct09.log NA
## S.npnct17.log NA
## S.npnct18.log NA
## S.npnct22.log NA
## S.npnct25.log NA
## S.npnct26.log NA
## S.npnct27.log NA
## S.npnct29.log NA
## S.npnct30.log NA
## S.P.http NA#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning in loop_apply(n, do.ply): Removed 16 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 16 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 16 rows containing missing values
## (geom_point).
print(subset(glb_feats_df, myNearZV))## id cor.y exclude.as.feat
## S.npnct21.log S.npnct21.log 2.760321e-02 0
## S.npnct23.log S.npnct23.log 2.760321e-02 0
## A.npnct21.log A.npnct21.log 1.537569e-02 0
## A.npnct23.log A.npnct23.log 1.537569e-02 0
## H.npnct03.log H.npnct03.log 9.533020e-03 0
## H.npnct24.log H.npnct24.log -9.890046e-19 0
## A.npnct25.log A.npnct25.log -5.547032e-03 0
## A.npnct10.log A.npnct10.log -5.547032e-03 0
## H.npnct10.log H.npnct10.log -5.547032e-03 0
## H.npnct20.log H.npnct20.log -5.547032e-03 0
## S.npnct02.log S.npnct02.log -5.547032e-03 0
## S.npnct10.log S.npnct10.log -5.547032e-03 0
## A.npnct07.log A.npnct07.log -1.083506e-02 0
## S.npnct07.log S.npnct07.log -1.083506e-02 0
## A.npnct05.log A.npnct05.log NA 0
## A.npnct09.log A.npnct09.log NA 0
## A.npnct22.log A.npnct22.log NA 0
## A.npnct26.log A.npnct26.log NA 0
## A.npnct27.log A.npnct27.log NA 0
## A.npnct29.log A.npnct29.log NA 0
## A.npnct30.log A.npnct30.log NA 0
## H.npnct09.log H.npnct09.log NA 0
## H.npnct17.log H.npnct17.log NA 0
## H.npnct18.log H.npnct18.log NA 0
## H.npnct21.log H.npnct21.log NA 0
## H.npnct22.log H.npnct22.log NA 0
## H.npnct23.log H.npnct23.log NA 0
## H.npnct25.log H.npnct25.log NA 0
## H.npnct26.log H.npnct26.log NA 0
## H.npnct27.log H.npnct27.log NA 0
## H.npnct29.log H.npnct29.log NA 0
## H.npnct30.log H.npnct30.log NA 0
## H.P.http H.P.http NA 1
## PubDate.year.fctr PubDate.year.fctr NA 0
## S.npnct05.log S.npnct05.log NA 0
## S.npnct09.log S.npnct09.log NA 0
## S.npnct17.log S.npnct17.log NA 0
## S.npnct18.log S.npnct18.log NA 0
## S.npnct22.log S.npnct22.log NA 0
## S.npnct25.log S.npnct25.log NA 0
## S.npnct26.log S.npnct26.log NA 0
## S.npnct27.log S.npnct27.log NA 0
## S.npnct29.log S.npnct29.log NA 0
## S.npnct30.log S.npnct30.log NA 0
## S.P.http S.P.http NA 1
## cor.y.abs cor.high.X freqRatio percentUnique
## S.npnct21.log 2.760321e-02 <NA> 6531.000 0.03061849
## S.npnct23.log 2.760321e-02 S.npnct21.log 6531.000 0.03061849
## A.npnct21.log 1.537569e-02 S.npnct21.log 3264.500 0.04592774
## A.npnct23.log 1.537569e-02 A.npnct21.log 3264.500 0.04592774
## H.npnct03.log 9.533020e-03 <NA> 2176.333 0.03061849
## H.npnct24.log 9.890046e-19 <NA> 0.000 0.01530925
## A.npnct25.log 5.547032e-03 <NA> 6531.000 0.03061849
## A.npnct10.log 5.547032e-03 <NA> 6531.000 0.03061849
## H.npnct10.log 5.547032e-03 <NA> 6531.000 0.03061849
## H.npnct20.log 5.547032e-03 <NA> 6531.000 0.03061849
## S.npnct02.log 5.547032e-03 <NA> 6531.000 0.03061849
## S.npnct10.log 5.547032e-03 <NA> 6531.000 0.03061849
## A.npnct07.log 1.083506e-02 <NA> 2176.000 0.04592774
## S.npnct07.log 1.083506e-02 A.npnct07.log 2176.000 0.04592774
## A.npnct05.log NA <NA> 0.000 0.01530925
## A.npnct09.log NA <NA> 0.000 0.01530925
## A.npnct22.log NA <NA> 0.000 0.01530925
## A.npnct26.log NA <NA> 0.000 0.01530925
## A.npnct27.log NA <NA> 0.000 0.01530925
## A.npnct29.log NA <NA> 0.000 0.01530925
## A.npnct30.log NA <NA> 0.000 0.01530925
## H.npnct09.log NA <NA> 0.000 0.01530925
## H.npnct17.log NA <NA> 0.000 0.01530925
## H.npnct18.log NA <NA> 0.000 0.01530925
## H.npnct21.log NA <NA> 0.000 0.01530925
## H.npnct22.log NA <NA> 0.000 0.01530925
## H.npnct23.log NA <NA> 0.000 0.01530925
## H.npnct25.log NA <NA> 0.000 0.01530925
## H.npnct26.log NA <NA> 0.000 0.01530925
## H.npnct27.log NA <NA> 0.000 0.01530925
## H.npnct29.log NA <NA> 0.000 0.01530925
## H.npnct30.log NA <NA> 0.000 0.01530925
## H.P.http NA <NA> 0.000 0.01530925
## PubDate.year.fctr NA <NA> 0.000 0.01530925
## S.npnct05.log NA <NA> 0.000 0.01530925
## S.npnct09.log NA <NA> 0.000 0.01530925
## S.npnct17.log NA <NA> 0.000 0.01530925
## S.npnct18.log NA <NA> 0.000 0.01530925
## S.npnct22.log NA <NA> 0.000 0.01530925
## S.npnct25.log NA <NA> 0.000 0.01530925
## S.npnct26.log NA <NA> 0.000 0.01530925
## S.npnct27.log NA <NA> 0.000 0.01530925
## S.npnct29.log NA <NA> 0.000 0.01530925
## S.npnct30.log NA <NA> 0.000 0.01530925
## S.P.http NA <NA> 0.000 0.01530925
## zeroVar nzv myNearZV is.cor.y.abs.low
## S.npnct21.log FALSE TRUE TRUE FALSE
## S.npnct23.log FALSE TRUE TRUE FALSE
## A.npnct21.log FALSE TRUE TRUE FALSE
## A.npnct23.log FALSE TRUE TRUE FALSE
## H.npnct03.log FALSE TRUE TRUE FALSE
## H.npnct24.log TRUE TRUE TRUE TRUE
## A.npnct25.log FALSE TRUE TRUE TRUE
## A.npnct10.log FALSE TRUE TRUE TRUE
## H.npnct10.log FALSE TRUE TRUE TRUE
## H.npnct20.log FALSE TRUE TRUE TRUE
## S.npnct02.log FALSE TRUE TRUE TRUE
## S.npnct10.log FALSE TRUE TRUE TRUE
## A.npnct07.log FALSE TRUE TRUE FALSE
## S.npnct07.log FALSE TRUE TRUE FALSE
## A.npnct05.log TRUE TRUE TRUE NA
## A.npnct09.log TRUE TRUE TRUE NA
## A.npnct22.log TRUE TRUE TRUE NA
## A.npnct26.log TRUE TRUE TRUE NA
## A.npnct27.log TRUE TRUE TRUE NA
## A.npnct29.log TRUE TRUE TRUE NA
## A.npnct30.log TRUE TRUE TRUE NA
## H.npnct09.log TRUE TRUE TRUE NA
## H.npnct17.log TRUE TRUE TRUE NA
## H.npnct18.log TRUE TRUE TRUE NA
## H.npnct21.log TRUE TRUE TRUE NA
## H.npnct22.log TRUE TRUE TRUE NA
## H.npnct23.log TRUE TRUE TRUE NA
## H.npnct25.log TRUE TRUE TRUE NA
## H.npnct26.log TRUE TRUE TRUE NA
## H.npnct27.log TRUE TRUE TRUE NA
## H.npnct29.log TRUE TRUE TRUE NA
## H.npnct30.log TRUE TRUE TRUE NA
## H.P.http TRUE TRUE TRUE NA
## PubDate.year.fctr TRUE TRUE TRUE NA
## S.npnct05.log TRUE TRUE TRUE NA
## S.npnct09.log TRUE TRUE TRUE NA
## S.npnct17.log TRUE TRUE TRUE NA
## S.npnct18.log TRUE TRUE TRUE NA
## S.npnct22.log TRUE TRUE TRUE NA
## S.npnct25.log TRUE TRUE TRUE NA
## S.npnct26.log TRUE TRUE TRUE NA
## S.npnct27.log TRUE TRUE TRUE NA
## S.npnct29.log TRUE TRUE TRUE NA
## S.npnct30.log TRUE TRUE TRUE NA
## S.P.http TRUE TRUE TRUE NAglb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## Popular Popular.fctr WordCount.log
## 1870 1870 109
## [1] "numeric data w/ 0s in : "
## WordCount Popular
## 109 5439
## PubDate.wkday.fctr PubDate.wkend
## 378 7624
## PubDate.last1 PubDate.last1.log
## 11 11
## PubDate.last10 PubDate.last10.log
## 10 10
## PubDate.last100 PubDate.last100.log
## 100 100
## PubDate.wkday.fctr.nonNA H.T.X2014
## 378 8243
## H.T.X2015 H.T.art
## 8244 8295
## H.T.bank H.T.big
## 8267 8297
## H.T.billion H.T.busi
## 8278 8260
## H.T.china H.T.daili
## 8278 8186
## H.T.day H.T.deal
## 8137 8291
## H.T.fashion H.T.first
## 8165 8275
## H.T.make H.T.morn
## 8299 8297
## H.T.new H.T.news
## 8102 8278
## H.T.newyork H.T.obama
## 8124 8260
## H.T.pictur H.T.polit
## 8287 8282
## H.T.report H.T.say
## 8127 8272
## H.T.springsumm H.T.take
## 8288 8294
## H.T.test H.T.today
## 8284 8168
## H.T.week S.T.articl
## 8132 8134
## S.T.can S.T.compani
## 8069 7928
## S.T.day S.T.fashion
## 8071 8138
## S.T.first S.T.intern
## 8132 8121
## S.T.make S.T.new
## 8112 7670
## S.T.newyork S.T.newyorktim
## 8102 8051
## S.T.one S.T.presid
## 8020 8055
## S.T.report S.T.said
## 8041 8057
## S.T.share S.T.show
## 8143 8138
## S.T.take S.T.time
## 8135 8114
## S.T.week S.T.will
## 7854 7667
## S.T.year A.T.articl
## 7816 8134
## A.T.can A.T.compani
## 8069 7928
## A.T.day A.T.fashion
## 8069 8138
## A.T.first A.T.intern
## 8132 8121
## A.T.make A.T.new
## 8111 7669
## A.T.newyork A.T.newyorktim
## 8102 8049
## A.T.one A.T.presid
## 8019 8055
## A.T.report A.T.said
## 8041 8057
## A.T.share A.T.show
## 8142 8138
## A.T.take A.T.time
## 8134 8113
## A.T.week A.T.will
## 7854 7665
## A.T.year H.T.clip
## 7816 8318
## H.T.ebola H.T.get
## 8314 8304
## H.T.newyorktim H.T.read
## 8350 8308
## H.T.word H.nwrds.unq.log
## 8305 3
## H.sum.TfIdf H.ratio.sum.TfIdf.nwrds
## 3 3
## H.nuppr.log H.ndgts.log
## 1 7174
## H.npnct01.log H.npnct02.log
## 8370 8387
## H.npnct04.log H.npnct05.log
## 8180 8389
## H.npnct06.log H.npnct07.log
## 8255 6580
## H.npnct08.log H.npnct11.log
## 8320 6788
## H.npnct12.log H.npnct13.log
## 7721 7580
## H.npnct14.log H.npnct15.log
## 8279 6691
## H.npnct16.log H.npnct19.log
## 8286 7892
## H.npnct28.log H.nstopwrds.log
## 8071 2183
## H.P.year.colon H.P.daily.clip.report
## 8147 8318
## H.P.fashion.week H.P.first.draft
## 8216 8328
## H.P.facts.figures H.P.friday.night.music
## 8394 8386
## H.P.no.comment.colon H.P.on.this.day
## 8392 8384
## H.P.quandary H.P.readers.respond
## 8390 8379
## H.P.recap.colon H.P.s.notebook
## 8324 8386
## H.P.today.in.politic H.P.today.in.smallbusiness
## 8336 8329
## H.P.verbatim.colon H.P.what.we.are
## 8357 8344
## S.T.appear S.T.archiv
## 8246 8176
## S.T.diari S.T.editor
## 8218 8206
## S.T.herald S.T.obama
## 8188 8165
## S.T.photo S.T.senat
## 8164 8160
## S.T.tribun S.T.word
## 8188 8218
## S.nwrds.log S.nwrds.unq.log
## 13 15
## S.sum.TfIdf S.ratio.sum.TfIdf.nwrds
## 15 15
## S.nchrs.log S.nuppr.log
## 13 14
## S.ndgts.log S.npnct01.log
## 6571 8371
## S.npnct03.log S.npnct04.log
## 8397 8049
## S.npnct06.log S.npnct08.log
## 8335 8354
## S.npnct11.log S.npnct12.log
## 3685 6770
## S.npnct13.log S.npnct14.log
## 429 8365
## S.npnct15.log S.npnct16.log
## 7801 8369
## S.npnct19.log S.npnct20.log
## 7716 8390
## S.npnct24.log S.npnct28.log
## 13 8324
## S.nstopwrds.log S.P.year.colon
## 58 8386
## S.P.daily.clip.report S.P.fashion.week
## 8318 8241
## S.P.first.draft S.P.metropolitan.diary.colon
## 8391 8319
## A.T.appear A.T.archiv
## 8246 8176
## A.T.diari A.T.editor
## 8218 8206
## A.T.herald A.T.obama
## 8188 8166
## A.T.photo A.T.senat
## 8167 8159
## A.T.tribun A.T.word
## 8188 8218
## A.nwrds.log A.nwrds.unq.log
## 17 19
## A.sum.TfIdf A.ratio.sum.TfIdf.nwrds
## 19 19
## A.nchrs.log A.nuppr.log
## 17 18
## A.ndgts.log A.npnct01.log
## 6567 8371
## A.npnct02.log A.npnct03.log
## 8394 8396
## A.npnct04.log A.npnct06.log
## 8050 8335
## A.npnct08.log A.npnct11.log
## 8353 3685
## A.npnct12.log A.npnct13.log
## 6767 434
## A.npnct14.log A.npnct15.log
## 8358 7797
## A.npnct16.log A.npnct17.log
## 8369 8394
## A.npnct18.log A.npnct19.log
## 8396 7712
## A.npnct20.log A.npnct24.log
## 8390 17
## A.npnct28.log A.nstopwrds.log
## 8324 62
## A.P.http A.P.year.colon
## 8396 8386
## A.P.daily.clip.report A.P.fashion.week
## 8318 8241
## A.P.first.draft A.P.metropolitan.diary.colon
## 8391 8319
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate myCategory
## 17 0 0glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 210.115 234.862 24.747
## 9 partition.data.training 6 0 234.863 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=1 - (nrow(glb_newobs_df) * 1.1 / nrow(glb_trnobs_df)))
glb_fitobs_df <- glb_trnobs_df[split, ]
glb_OOBobs_df <- glb_trnobs_df[!split ,]
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## Loading required package: caToolsif (!is.null(glb_max_fitent_obs) && (nrow(glb_fitobs_df) > glb_max_fitent_obs)) {
warning("glb_fitobs_df restricted to glb_max_fitent_obs: ",
format(glb_max_fitent_obs, big.mark=","))
org_fitent_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitent_df[split <- sample.split(org_fitent_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitent_obs), ]
org_fitent_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_vars] %in%
glb_fitobs_df[, glb_id_vars], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_vars] %in%
glb_OOBobs_df[, glb_id_vars], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_vars)) {
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newent_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
"myCategory")
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
"myCategory")
glb_ctgry_df <- merge(newent_ctgry_df, OOBobs_ctgry_df, by="myCategory", all=TRUE,
suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}## Popular.0 Popular.1 Popular.NA
## NA NA 1870
## Fit 3726 749 NA
## OOB 1713 344 NA
## Popular.0 Popular.1 Popular.NA
## NA NA 1
## Fit 0.8326257 0.1673743 NA
## OOB 0.8327662 0.1672338 NA
## myCategory .n.Tst .n.OOB .freqRatio.Tst
## 1 ## 342 410 0.182887701
## 6 Business#Business Day#Dealbook 304 312 0.162566845
## 10 Culture#Arts# 244 225 0.130481283
## 15 OpEd#Opinion# 164 154 0.087700535
## 9 Business#Technology# 114 114 0.060962567
## 20 TStyle## 105 221 0.056149733
## 5 #U.S.#Education 89 93 0.047593583
## 13 Metro#N.Y. / Region# 67 60 0.035828877
## 18 Styles#U.S.# 61 54 0.032620321
## 16 Science#Health# 57 66 0.030481283
## 12 Foreign#World#Asia Pacific 56 61 0.029946524
## 2 #Multimedia# 52 42 0.027807487
## 11 Foreign#World# 47 47 0.025133690
## 8 Business#Crosswords/Games# 42 40 0.022459893
## 7 Business#Business Day#Small Business 41 45 0.021925134
## 19 Travel#Travel# 35 31 0.018716578
## 3 #Opinion#Room For Debate 20 18 0.010695187
## 17 Styles##Fashion 15 41 0.008021390
## 4 #Opinion#The Public Editor 10 10 0.005347594
## 14 myOther 5 13 0.002673797
## .freqRatio.OOB
## 1 0.199319397
## 6 0.151677200
## 10 0.109382596
## 15 0.074866310
## 9 0.055420515
## 20 0.107438017
## 5 0.045211473
## 13 0.029168692
## 18 0.026251823
## 16 0.032085561
## 12 0.029654837
## 2 0.020418085
## 11 0.022848809
## 8 0.019445795
## 7 0.021876519
## 19 0.015070491
## 3 0.008750608
## 17 0.019931940
## 4 0.004861449
## 14 0.006319883# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 280 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_vars) && glb_id_vars != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_vars, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## Popular.fctr Popular.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## Popular Popular 1.00000000 TRUE 1.00000000 <NA>
## UniqueID UniqueID 0.01182492 TRUE 0.01182492 <NA>
## Popular.fctr Popular.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV
## Popular 4.976212 0.03061849 FALSE FALSE FALSE
## UniqueID 1.000000 100.00000000 FALSE FALSE FALSE
## Popular.fctr NA NA NA NA NA
## is.cor.y.abs.low interaction.feat rsp_var_raw id_var rsp_var
## Popular FALSE <NA> TRUE NA NA
## UniqueID FALSE <NA> FALSE TRUE NA
## Popular.fctr NA <NA> NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## [1] "S.npnct21.log" "S.npnct23.log" "A.npnct21.log"
## [4] "A.npnct23.log" "H.npnct03.log" "H.npnct24.log"
## [7] "A.npnct25.log" "A.npnct10.log" "H.npnct10.log"
## [10] "H.npnct20.log" "S.npnct02.log" "S.npnct10.log"
## [13] "A.npnct07.log" "S.npnct07.log" "A.npnct05.log"
## [16] "A.npnct09.log" "A.npnct22.log" "A.npnct26.log"
## [19] "A.npnct27.log" "A.npnct29.log" "A.npnct30.log"
## [22] "H.npnct09.log" "H.npnct17.log" "H.npnct18.log"
## [25] "H.npnct21.log" "H.npnct22.log" "H.npnct23.log"
## [28] "H.npnct25.log" "H.npnct26.log" "H.npnct27.log"
## [31] "H.npnct29.log" "H.npnct30.log" "H.P.http"
## [34] "PubDate.year.fctr" "S.npnct05.log" "S.npnct09.log"
## [37] "S.npnct17.log" "S.npnct18.log" "S.npnct22.log"
## [40] "S.npnct25.log" "S.npnct26.log" "S.npnct27.log"
## [43] "S.npnct29.log" "S.npnct30.log" "S.P.http"print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 8402 246print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 6532 290print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 4475 290print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 2057 290print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 1870 290# # Does not handle NULL or length(glb_id_vars) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_vars] %in% glb_trnobs_df[, glb_id_vars],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_vars] %in% glb_fitobs_df[, glb_id_vars],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_vars] %in% glb_OOBobs_df[, glb_id_vars],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_vars] %in% glb_newobs_df[, glb_id_vars],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 234.863 235.829 0.966
## 10 fit.models 7 0 235.830 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a lower correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl_fn(model_id="Baseline", model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.8326257 0.1673743
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8326257 0.1673743
## 2 0.8326257 0.1673743
## 3 0.8326257 0.1673743
## 4 0.8326257 0.1673743
## 5 0.8326257 0.1673743
## 6 0.8326257 0.1673743
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.MFO.myMFO_classfr.N
## 1 N 3726
## 2 Y 749
## Prediction
## Reference N Y
## N 3726 0
## Y 749 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.326257e-01 0.000000e+00 8.213602e-01 8.434553e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 5.097571e-01 1.800616e-164
## [1] " calling mypredict_mdl for OOB:"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8326257 0.1673743
## 2 0.8326257 0.1673743
## 3 0.8326257 0.1673743
## 4 0.8326257 0.1673743
## 5 0.8326257 0.1673743
## 6 0.8326257 0.1673743
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.MFO.myMFO_classfr.N
## 1 N 1713
## 2 Y 344
## Prediction
## Reference N Y
## N 1713 0
## Y 344 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.327662e-01 0.000000e+00 8.159247e-01 8.486533e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 5.143944e-01 2.337097e-76
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.004 0.003 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8326257
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8213602 0.8434553 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8327662
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8159247 0.8486533 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.2867534
## 3 0.2 0.1746905
## 4 0.3 0.1746905
## 5 0.4 0.1746905
## 6 0.5 0.1746905
## 7 0.6 0.1746905
## 8 0.7 0.1746905
## 9 0.8 0.1746905
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Random.myrandom_classfr.Y
## 1 N 3726
## 2 Y 749
## Prediction
## Reference N Y
## N 0 3726
## Y 0 749
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.1673743 0.0000000 0.1565447 0.1786398 0.8326257
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] " calling mypredict_mdl for OOB:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.2865473
## 3 0.2 0.1553398
## 4 0.3 0.1553398
## 5 0.4 0.1553398
## 6 0.5 0.1553398
## 7 0.6 0.1553398
## 8 0.7 0.1553398
## 9 0.8 0.1553398
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Random.myrandom_classfr.Y
## 1 N 1713
## 2 Y 344
## Prediction
## Reference N Y
## N 0 1713
## Y 0 344
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.1672338 0.0000000 0.1513467 0.1840753 0.8327662
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.38 0.002 0.5072166
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.1 0.2867534 0.1673743
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.1565447 0.1786398 0 0.4877001
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.2865473 0.1672338
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.1513467 0.1840753 0# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: A.nuppr.log, A.ratio.sum.TfIdf.nwrds"## Loading required package: rpart## Fitting cp = 0.0441 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4475
##
## CP nsplit rel error
## 1 0.04405874 0 1
##
## Node number 1: 4475 observations
## predicted class=N expected loss=0.1673743 P(node) =1
## class counts: 3726 749
## probabilities: 0.833 0.167
##
## n= 4475
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4475 749 N (0.8326257 0.1673743) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 3726
## 2 Y 749
## Prediction
## Reference N Y
## N 3726 0
## Y 749 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.326257e-01 0.000000e+00 8.213602e-01 8.434553e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 5.097571e-01 1.800616e-164
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 1713
## 2 Y 344
## Prediction
## Reference N Y
## N 1713 0
## Y 344 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.327662e-01 0.000000e+00 8.159247e-01 8.486533e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 5.143944e-01 2.337097e-76
## model_id model_method feats
## 1 Max.cor.Y.cv.0.rpart rpart A.nuppr.log, A.ratio.sum.TfIdf.nwrds
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.808 0.09
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.8326257
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8213602 0.8434553 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8327662
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8159247 0.8486533 0ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: A.nuppr.log, A.ratio.sum.TfIdf.nwrds"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4475
##
## CP nsplit rel error
## 1 0.0440587450 0 1.0000000
## 2 0.0046728972 2 0.9118825
## 3 0.0040053405 4 0.9025367
## 4 0.0022251891 11 0.8731642
## 5 0.0020026702 14 0.8664887
## 6 0.0013351135 18 0.8584780
## 7 0.0011125946 21 0.8544726
## 8 0.0006675567 44 0.8184246
## 9 0.0003337784 54 0.8117490
## 10 0.0000000000 62 0.8090788
##
## Variable importance
## A.ratio.sum.TfIdf.nwrds A.nuppr.log
## 63 37
##
## Node number 1: 4475 observations, complexity param=0.04405874
## predicted class=N expected loss=0.1673743 P(node) =1
## class counts: 3726 749
## probabilities: 0.833 0.167
## left son=2 (2625 obs) right son=3 (1850 obs)
## Primary splits:
## A.nuppr.log < 1.497866 to the right, improve=85.47560, (0 missing)
## A.ratio.sum.TfIdf.nwrds < 1.155894 to the left, improve=85.14662, (0 missing)
## Surrogate splits:
## A.ratio.sum.TfIdf.nwrds < 0.6475191 to the left, agree=0.645, adj=0.142, (0 split)
##
## Node number 2: 2625 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.08533333 P(node) =0.5865922
## class counts: 2401 224
## probabilities: 0.915 0.085
## left son=4 (1049 obs) right son=5 (1576 obs)
## Primary splits:
## A.nuppr.log < 2.012676 to the right, improve=3.357270, (0 missing)
## A.ratio.sum.TfIdf.nwrds < 1.333358 to the left, improve=1.653784, (0 missing)
## Surrogate splits:
## A.ratio.sum.TfIdf.nwrds < 0.2037625 to the left, agree=0.61, adj=0.025, (0 split)
##
## Node number 3: 1850 observations, complexity param=0.04405874
## predicted class=N expected loss=0.2837838 P(node) =0.4134078
## class counts: 1325 525
## probabilities: 0.716 0.284
## left son=6 (1706 obs) right son=7 (144 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 1.129992 to the left, improve=61.95156, (0 missing)
## A.nuppr.log < 1.242453 to the right, improve= 8.43495, (0 missing)
##
## Node number 4: 1049 observations
## predicted class=N expected loss=0.05433746 P(node) =0.2344134
## class counts: 992 57
## probabilities: 0.946 0.054
##
## Node number 5: 1576 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.1059645 P(node) =0.3521788
## class counts: 1409 167
## probabilities: 0.894 0.106
## left son=10 (1569 obs) right son=11 (7 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 1.310269 to the left, improve=1.4635540, (0 missing)
## A.nuppr.log < 1.700599 to the right, improve=0.4065705, (0 missing)
##
## Node number 6: 1706 observations, complexity param=0.00400534
## predicted class=N expected loss=0.2461899 P(node) =0.3812291
## class counts: 1286 420
## probabilities: 0.754 0.246
## left son=12 (1427 obs) right son=13 (279 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.6709461 to the left, improve=15.343650, (0 missing)
## A.nuppr.log < 1.242453 to the right, improve= 6.097801, (0 missing)
##
## Node number 7: 144 observations, complexity param=0.004672897
## predicted class=Y expected loss=0.2708333 P(node) =0.03217877
## class counts: 39 105
## probabilities: 0.271 0.729
## left son=14 (50 obs) right son=15 (94 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 1.389273 to the left, improve=3.408617, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=2.074295, (0 missing)
##
## Node number 10: 1569 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.1045252 P(node) =0.3506145
## class counts: 1405 164
## probabilities: 0.895 0.105
## left son=20 (1431 obs) right son=21 (138 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.660439 to the left, improve=0.9119264, (0 missing)
## A.nuppr.log < 1.700599 to the right, improve=0.3585603, (0 missing)
##
## Node number 11: 7 observations
## predicted class=N expected loss=0.4285714 P(node) =0.001564246
## class counts: 4 3
## probabilities: 0.571 0.429
##
## Node number 12: 1427 observations, complexity param=0.001112595
## predicted class=N expected loss=0.2165382 P(node) =0.3188827
## class counts: 1118 309
## probabilities: 0.783 0.217
## left son=24 (496 obs) right son=25 (931 obs)
## Primary splits:
## A.nuppr.log < 1.242453 to the right, improve=7.746426, (0 missing)
## A.ratio.sum.TfIdf.nwrds < 0.5877074 to the right, improve=2.910878, (0 missing)
##
## Node number 13: 279 observations, complexity param=0.00400534
## predicted class=N expected loss=0.3978495 P(node) =0.06234637
## class counts: 168 111
## probabilities: 0.602 0.398
## left son=26 (8 obs) right son=27 (271 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 1.094536 to the right, improve=2.607309, (0 missing)
## A.nuppr.log < 1.242453 to the left, improve=1.733233, (0 missing)
##
## Node number 14: 50 observations, complexity param=0.004672897
## predicted class=Y expected loss=0.42 P(node) =0.01117318
## class counts: 21 29
## probabilities: 0.420 0.580
## left son=28 (29 obs) right son=29 (21 obs)
## Primary splits:
## A.nuppr.log < 0.8958797 to the left, improve=5.561970, (0 missing)
## A.ratio.sum.TfIdf.nwrds < 1.2749 to the right, improve=1.033611, (0 missing)
## Surrogate splits:
## A.ratio.sum.TfIdf.nwrds < 1.164653 to the right, agree=0.6, adj=0.048, (0 split)
##
## Node number 15: 94 observations
## predicted class=Y expected loss=0.1914894 P(node) =0.02100559
## class counts: 18 76
## probabilities: 0.191 0.809
##
## Node number 20: 1431 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.09923131 P(node) =0.3197765
## class counts: 1289 142
## probabilities: 0.901 0.099
## left son=40 (1119 obs) right son=41 (312 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.2666657 to the right, improve=0.9991022, (0 missing)
## A.nuppr.log < 1.700599 to the right, improve=0.2108938, (0 missing)
##
## Node number 21: 138 observations
## predicted class=N expected loss=0.1594203 P(node) =0.03083799
## class counts: 116 22
## probabilities: 0.841 0.159
##
## Node number 24: 496 observations, complexity param=0.0003337784
## predicted class=N expected loss=0.1451613 P(node) =0.110838
## class counts: 424 72
## probabilities: 0.855 0.145
## left son=48 (481 obs) right son=49 (15 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.2080528 to the right, improve=1.095388, (0 missing)
##
## Node number 25: 931 observations, complexity param=0.001112595
## predicted class=N expected loss=0.254565 P(node) =0.2080447
## class counts: 694 237
## probabilities: 0.745 0.255
## left son=50 (86 obs) right son=51 (845 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.5918449 to the right, improve=3.623909, (0 missing)
## A.nuppr.log < 0.3465736 to the left, improve=1.045840, (0 missing)
##
## Node number 26: 8 observations
## predicted class=N expected loss=0 P(node) =0.001787709
## class counts: 8 0
## probabilities: 1.000 0.000
##
## Node number 27: 271 observations, complexity param=0.00400534
## predicted class=N expected loss=0.4095941 P(node) =0.06055866
## class counts: 160 111
## probabilities: 0.590 0.410
## left son=54 (198 obs) right son=55 (73 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.9205242 to the left, improve=1.890091, (0 missing)
## A.nuppr.log < 1.242453 to the left, improve=1.824857, (0 missing)
##
## Node number 28: 29 observations, complexity param=0.001335113
## predicted class=N expected loss=0.3793103 P(node) =0.006480447
## class counts: 18 11
## probabilities: 0.621 0.379
## left son=56 (8 obs) right son=57 (21 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 1.186261 to the left, improve=0.3694581, (0 missing)
##
## Node number 29: 21 observations
## predicted class=Y expected loss=0.1428571 P(node) =0.004692737
## class counts: 3 18
## probabilities: 0.143 0.857
##
## Node number 40: 1119 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.0893655 P(node) =0.2500559
## class counts: 1019 100
## probabilities: 0.911 0.089
## left son=80 (469 obs) right son=81 (650 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.3740436 to the left, improve=0.7213291, (0 missing)
## A.nuppr.log < 1.868835 to the right, improve=0.2545775, (0 missing)
##
## Node number 41: 312 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.1346154 P(node) =0.06972067
## class counts: 270 42
## probabilities: 0.865 0.135
## left son=82 (305 obs) right son=83 (7 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.2659316 to the left, improve=2.7325890, (0 missing)
## A.nuppr.log < 1.700599 to the right, improve=0.7920709, (0 missing)
##
## Node number 48: 481 observations, complexity param=0.0003337784
## predicted class=N expected loss=0.1392931 P(node) =0.107486
## class counts: 414 67
## probabilities: 0.861 0.139
## left son=96 (31 obs) right son=97 (450 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.5798804 to the right, improve=0.3705616, (0 missing)
##
## Node number 49: 15 observations
## predicted class=N expected loss=0.3333333 P(node) =0.003351955
## class counts: 10 5
## probabilities: 0.667 0.333
##
## Node number 50: 86 observations, complexity param=0.001112595
## predicted class=N expected loss=0.1162791 P(node) =0.01921788
## class counts: 76 10
## probabilities: 0.884 0.116
## left son=100 (36 obs) right son=101 (50 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.6126658 to the left, improve=0.9699742, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.5788174, (0 missing)
##
## Node number 51: 845 observations, complexity param=0.001112595
## predicted class=N expected loss=0.2686391 P(node) =0.1888268
## class counts: 618 227
## probabilities: 0.731 0.269
## left son=102 (209 obs) right son=103 (636 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.298779 to the left, improve=2.197059, (0 missing)
## A.nuppr.log < 0.3465736 to the left, improve=1.165707, (0 missing)
## Surrogate splits:
## A.nuppr.log < 0.3465736 to the left, agree=0.762, adj=0.038, (0 split)
##
## Node number 54: 198 observations, complexity param=0.00400534
## predicted class=N expected loss=0.3737374 P(node) =0.04424581
## class counts: 124 74
## probabilities: 0.626 0.374
## left son=108 (113 obs) right son=109 (85 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.7487838 to the right, improve=3.514146, (0 missing)
## A.nuppr.log < 1.242453 to the left, improve=1.910553, (0 missing)
##
## Node number 55: 73 observations, complexity param=0.00400534
## predicted class=Y expected loss=0.4931507 P(node) =0.01631285
## class counts: 36 37
## probabilities: 0.493 0.507
## left son=110 (64 obs) right son=111 (9 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.9413812 to the right, improve=2.9966230, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.1743537, (0 missing)
##
## Node number 56: 8 observations
## predicted class=N expected loss=0.25 P(node) =0.001787709
## class counts: 6 2
## probabilities: 0.750 0.250
##
## Node number 57: 21 observations, complexity param=0.001335113
## predicted class=N expected loss=0.4285714 P(node) =0.004692737
## class counts: 12 9
## probabilities: 0.571 0.429
## left son=114 (13 obs) right son=115 (8 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 1.261906 to the right, improve=0.9972527, (0 missing)
##
## Node number 80: 469 observations
## predicted class=N expected loss=0.06823028 P(node) =0.1048045
## class counts: 437 32
## probabilities: 0.932 0.068
##
## Node number 81: 650 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.1046154 P(node) =0.1452514
## class counts: 582 68
## probabilities: 0.895 0.105
## left son=162 (419 obs) right son=163 (231 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4375274 to the right, improve=1.2988650, (0 missing)
## A.nuppr.log < 1.868835 to the right, improve=0.3641148, (0 missing)
##
## Node number 82: 305 observations
## predicted class=N expected loss=0.1245902 P(node) =0.06815642
## class counts: 267 38
## probabilities: 0.875 0.125
##
## Node number 83: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.001564246
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 96: 31 observations
## predicted class=N expected loss=0.06451613 P(node) =0.006927374
## class counts: 29 2
## probabilities: 0.935 0.065
##
## Node number 97: 450 observations, complexity param=0.0003337784
## predicted class=N expected loss=0.1444444 P(node) =0.1005587
## class counts: 385 65
## probabilities: 0.856 0.144
## left son=194 (382 obs) right son=195 (68 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4830579 to the left, improve=0.6047292, (0 missing)
##
## Node number 100: 36 observations
## predicted class=N expected loss=0.02777778 P(node) =0.008044693
## class counts: 35 1
## probabilities: 0.972 0.028
##
## Node number 101: 50 observations, complexity param=0.001112595
## predicted class=N expected loss=0.18 P(node) =0.01117318
## class counts: 41 9
## probabilities: 0.820 0.180
## left son=202 (42 obs) right son=203 (8 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.6179032 to the right, improve=3.7719050, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.3266667, (0 missing)
##
## Node number 102: 209 observations
## predicted class=N expected loss=0.2057416 P(node) =0.04670391
## class counts: 166 43
## probabilities: 0.794 0.206
##
## Node number 103: 636 observations, complexity param=0.001112595
## predicted class=N expected loss=0.2893082 P(node) =0.1421229
## class counts: 452 184
## probabilities: 0.711 0.289
## left son=206 (625 obs) right son=207 (11 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.3026072 to the right, improve=2.69648200, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.02010568, (0 missing)
##
## Node number 108: 113 observations, complexity param=0.00200267
## predicted class=N expected loss=0.2920354 P(node) =0.0252514
## class counts: 80 33
## probabilities: 0.708 0.292
## left son=216 (29 obs) right son=217 (84 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.7796549 to the left, improve=2.774925, (0 missing)
## A.nuppr.log < 1.242453 to the left, improve=0.748391, (0 missing)
##
## Node number 109: 85 observations, complexity param=0.00400534
## predicted class=N expected loss=0.4823529 P(node) =0.01899441
## class counts: 44 41
## probabilities: 0.518 0.482
## left son=218 (44 obs) right son=219 (41 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.7088626 to the left, improve=3.649941, (0 missing)
## A.nuppr.log < 1.242453 to the left, improve=1.042297, (0 missing)
## Surrogate splits:
## A.nuppr.log < 1.242453 to the left, agree=0.553, adj=0.073, (0 split)
##
## Node number 110: 64 observations, complexity param=0.00400534
## predicted class=N expected loss=0.453125 P(node) =0.01430168
## class counts: 35 29
## probabilities: 0.547 0.453
## left son=220 (26 obs) right son=221 (38 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.9880696 to the left, improve=1.85235300, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.09313424, (0 missing)
##
## Node number 111: 9 observations
## predicted class=Y expected loss=0.1111111 P(node) =0.002011173
## class counts: 1 8
## probabilities: 0.111 0.889
##
## Node number 114: 13 observations
## predicted class=N expected loss=0.3076923 P(node) =0.002905028
## class counts: 9 4
## probabilities: 0.692 0.308
##
## Node number 115: 8 observations
## predicted class=Y expected loss=0.375 P(node) =0.001787709
## class counts: 3 5
## probabilities: 0.375 0.625
##
## Node number 162: 419 observations
## predicted class=N expected loss=0.08114558 P(node) =0.09363128
## class counts: 385 34
## probabilities: 0.919 0.081
##
## Node number 163: 231 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.1471861 P(node) =0.05162011
## class counts: 197 34
## probabilities: 0.853 0.147
## left son=326 (224 obs) right son=327 (7 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4355984 to the left, improve=4.6431280, (0 missing)
## A.nuppr.log < 1.700599 to the left, improve=0.3499585, (0 missing)
##
## Node number 194: 382 observations, complexity param=0.0003337784
## predicted class=N expected loss=0.1335079 P(node) =0.08536313
## class counts: 331 51
## probabilities: 0.866 0.134
## left son=388 (18 obs) right son=389 (364 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4603122 to the right, improve=0.6734077, (0 missing)
##
## Node number 195: 68 observations, complexity param=0.0003337784
## predicted class=N expected loss=0.2058824 P(node) =0.01519553
## class counts: 54 14
## probabilities: 0.794 0.206
## left son=390 (44 obs) right son=391 (24 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.5141319 to the right, improve=0.5459002, (0 missing)
##
## Node number 202: 42 observations
## predicted class=N expected loss=0.0952381 P(node) =0.009385475
## class counts: 38 4
## probabilities: 0.905 0.095
##
## Node number 203: 8 observations
## predicted class=Y expected loss=0.375 P(node) =0.001787709
## class counts: 3 5
## probabilities: 0.375 0.625
##
## Node number 206: 625 observations, complexity param=0.001112595
## predicted class=N expected loss=0.2832 P(node) =0.1396648
## class counts: 448 177
## probabilities: 0.717 0.283
## left son=412 (212 obs) right son=413 (413 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4728175 to the right, improve=2.06900500, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.05904997, (0 missing)
##
## Node number 207: 11 observations
## predicted class=Y expected loss=0.3636364 P(node) =0.002458101
## class counts: 4 7
## probabilities: 0.364 0.636
##
## Node number 216: 29 observations
## predicted class=N expected loss=0.1034483 P(node) =0.006480447
## class counts: 26 3
## probabilities: 0.897 0.103
##
## Node number 217: 84 observations, complexity param=0.00200267
## predicted class=N expected loss=0.3571429 P(node) =0.01877095
## class counts: 54 30
## probabilities: 0.643 0.357
## left son=434 (65 obs) right son=435 (19 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.8074537 to the right, improve=2.4159630, (0 missing)
## A.nuppr.log < 1.242453 to the left, improve=0.4526786, (0 missing)
##
## Node number 218: 44 observations, complexity param=0.001335113
## predicted class=N expected loss=0.3409091 P(node) =0.009832402
## class counts: 29 15
## probabilities: 0.659 0.341
## left son=436 (35 obs) right son=437 (9 obs)
## Primary splits:
## A.nuppr.log < 1.242453 to the left, improve=1.0425690, (0 missing)
## A.ratio.sum.TfIdf.nwrds < 0.7025594 to the right, improve=0.9116162, (0 missing)
##
## Node number 219: 41 observations, complexity param=0.002225189
## predicted class=Y expected loss=0.3658537 P(node) =0.009162011
## class counts: 15 26
## probabilities: 0.366 0.634
## left son=438 (30 obs) right son=439 (11 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.7198596 to the right, improve=1.0183300, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.7468783, (0 missing)
##
## Node number 220: 26 observations
## predicted class=N expected loss=0.3076923 P(node) =0.005810056
## class counts: 18 8
## probabilities: 0.692 0.308
##
## Node number 221: 38 observations, complexity param=0.00200267
## predicted class=Y expected loss=0.4473684 P(node) =0.00849162
## class counts: 17 21
## probabilities: 0.447 0.553
## left son=442 (31 obs) right son=443 (7 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.9961147 to the right, improve=0.44845990, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.01845919, (0 missing)
##
## Node number 326: 224 observations
## predicted class=N expected loss=0.1294643 P(node) =0.05005587
## class counts: 195 29
## probabilities: 0.871 0.129
##
## Node number 327: 7 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.001564246
## class counts: 2 5
## probabilities: 0.286 0.714
##
## Node number 388: 18 observations
## predicted class=N expected loss=0 P(node) =0.004022346
## class counts: 18 0
## probabilities: 1.000 0.000
##
## Node number 389: 364 observations, complexity param=0.0003337784
## predicted class=N expected loss=0.1401099 P(node) =0.08134078
## class counts: 313 51
## probabilities: 0.860 0.140
## left son=778 (309 obs) right son=779 (55 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4087233 to the left, improve=0.4647783, (0 missing)
##
## Node number 390: 44 observations
## predicted class=N expected loss=0.1590909 P(node) =0.009832402
## class counts: 37 7
## probabilities: 0.841 0.159
##
## Node number 391: 24 observations, complexity param=0.0003337784
## predicted class=N expected loss=0.2916667 P(node) =0.005363128
## class counts: 17 7
## probabilities: 0.708 0.292
## left son=782 (17 obs) right son=783 (7 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.5060435 to the left, improve=1.546919, (0 missing)
##
## Node number 412: 212 observations, complexity param=0.001112595
## predicted class=N expected loss=0.2264151 P(node) =0.0473743
## class counts: 164 48
## probabilities: 0.774 0.226
## left son=824 (27 obs) right son=825 (185 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4829834 to the left, improve=2.21930600, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.02646978, (0 missing)
##
## Node number 413: 413 observations, complexity param=0.001112595
## predicted class=N expected loss=0.3123487 P(node) =0.0922905
## class counts: 284 129
## probabilities: 0.688 0.312
## left son=826 (36 obs) right son=827 (377 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.3165757 to the left, improve=2.3732240, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.1372912, (0 missing)
##
## Node number 434: 65 observations
## predicted class=N expected loss=0.2923077 P(node) =0.01452514
## class counts: 46 19
## probabilities: 0.708 0.292
##
## Node number 435: 19 observations
## predicted class=Y expected loss=0.4210526 P(node) =0.00424581
## class counts: 8 11
## probabilities: 0.421 0.579
##
## Node number 436: 35 observations
## predicted class=N expected loss=0.2857143 P(node) =0.007821229
## class counts: 25 10
## probabilities: 0.714 0.286
##
## Node number 437: 9 observations
## predicted class=Y expected loss=0.4444444 P(node) =0.002011173
## class counts: 4 5
## probabilities: 0.444 0.556
##
## Node number 438: 30 observations, complexity param=0.002225189
## predicted class=Y expected loss=0.4333333 P(node) =0.006703911
## class counts: 13 17
## probabilities: 0.433 0.567
## left son=876 (23 obs) right son=877 (7 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.7443823 to the left, improve=1.5407870, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.7242836, (0 missing)
##
## Node number 439: 11 observations
## predicted class=Y expected loss=0.1818182 P(node) =0.002458101
## class counts: 2 9
## probabilities: 0.182 0.818
##
## Node number 442: 31 observations, complexity param=0.00200267
## predicted class=Y expected loss=0.483871 P(node) =0.006927374
## class counts: 15 16
## probabilities: 0.484 0.516
## left son=884 (17 obs) right son=885 (14 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 1.051372 to the left, improve=0.8200054, (0 missing)
## A.nuppr.log < 1.242453 to the right, improve=0.1303356, (0 missing)
##
## Node number 443: 7 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.001564246
## class counts: 2 5
## probabilities: 0.286 0.714
##
## Node number 778: 309 observations
## predicted class=N expected loss=0.1294498 P(node) =0.06905028
## class counts: 269 40
## probabilities: 0.871 0.129
##
## Node number 779: 55 observations, complexity param=0.0003337784
## predicted class=N expected loss=0.2 P(node) =0.0122905
## class counts: 44 11
## probabilities: 0.800 0.200
## left son=1558 (48 obs) right son=1559 (7 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4152543 to the right, improve=2.213095, (0 missing)
##
## Node number 782: 17 observations
## predicted class=N expected loss=0.1764706 P(node) =0.003798883
## class counts: 14 3
## probabilities: 0.824 0.176
##
## Node number 783: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.001564246
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 824: 27 observations
## predicted class=N expected loss=0.03703704 P(node) =0.00603352
## class counts: 26 1
## probabilities: 0.963 0.037
##
## Node number 825: 185 observations, complexity param=0.001112595
## predicted class=N expected loss=0.2540541 P(node) =0.04134078
## class counts: 138 47
## probabilities: 0.746 0.254
## left son=1650 (178 obs) right son=1651 (7 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4857374 to the right, improve=3.0820010, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.1470979, (0 missing)
##
## Node number 826: 36 observations
## predicted class=N expected loss=0.1388889 P(node) =0.008044693
## class counts: 31 5
## probabilities: 0.861 0.139
##
## Node number 827: 377 observations, complexity param=0.001112595
## predicted class=N expected loss=0.3289125 P(node) =0.08424581
## class counts: 253 124
## probabilities: 0.671 0.329
## left son=1654 (274 obs) right son=1655 (103 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.355064 to the right, improve=1.762427, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.358814, (0 missing)
##
## Node number 876: 23 observations, complexity param=0.002225189
## predicted class=N expected loss=0.4782609 P(node) =0.005139665
## class counts: 12 11
## probabilities: 0.522 0.478
## left son=1752 (13 obs) right son=1753 (10 obs)
## Primary splits:
## A.nuppr.log < 0.8958797 to the left, improve=1.7397990, (0 missing)
## A.ratio.sum.TfIdf.nwrds < 0.728776 to the right, improve=0.5282609, (0 missing)
## Surrogate splits:
## A.ratio.sum.TfIdf.nwrds < 0.7265897 to the left, agree=0.652, adj=0.2, (0 split)
##
## Node number 877: 7 observations
## predicted class=Y expected loss=0.1428571 P(node) =0.001564246
## class counts: 1 6
## probabilities: 0.143 0.857
##
## Node number 884: 17 observations
## predicted class=N expected loss=0.4117647 P(node) =0.003798883
## class counts: 10 7
## probabilities: 0.588 0.412
##
## Node number 885: 14 observations
## predicted class=Y expected loss=0.3571429 P(node) =0.003128492
## class counts: 5 9
## probabilities: 0.357 0.643
##
## Node number 1558: 48 observations
## predicted class=N expected loss=0.1458333 P(node) =0.01072626
## class counts: 41 7
## probabilities: 0.854 0.146
##
## Node number 1559: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.001564246
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 1650: 178 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.2359551 P(node) =0.03977654
## class counts: 136 42
## probabilities: 0.764 0.236
## left son=3300 (144 obs) right son=3301 (34 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.5655899 to the left, improve=1.1503640, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.3949456, (0 missing)
##
## Node number 1651: 7 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.001564246
## class counts: 2 5
## probabilities: 0.286 0.714
##
## Node number 1654: 274 observations, complexity param=0.001112595
## predicted class=N expected loss=0.2992701 P(node) =0.06122905
## class counts: 192 82
## probabilities: 0.701 0.299
## left son=3308 (223 obs) right son=3309 (51 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4448867 to the left, improve=1.5860230, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.7046543, (0 missing)
##
## Node number 1655: 103 observations, complexity param=0.001112595
## predicted class=N expected loss=0.407767 P(node) =0.02301676
## class counts: 61 42
## probabilities: 0.592 0.408
## left son=3310 (61 obs) right son=3311 (42 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.342641 to the left, improve=1.90994600, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.07485929, (0 missing)
##
## Node number 1752: 13 observations
## predicted class=N expected loss=0.3076923 P(node) =0.002905028
## class counts: 9 4
## probabilities: 0.692 0.308
##
## Node number 1753: 10 observations
## predicted class=Y expected loss=0.3 P(node) =0.002234637
## class counts: 3 7
## probabilities: 0.300 0.700
##
## Node number 3300: 144 observations
## predicted class=N expected loss=0.2083333 P(node) =0.03217877
## class counts: 114 30
## probabilities: 0.792 0.208
##
## Node number 3301: 34 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.3529412 P(node) =0.007597765
## class counts: 22 12
## probabilities: 0.647 0.353
## left son=6602 (25 obs) right son=6603 (9 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.5711564 to the right, improve=1.0049670, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.1188854, (0 missing)
##
## Node number 3308: 223 observations, complexity param=0.001112595
## predicted class=N expected loss=0.2735426 P(node) =0.0498324
## class counts: 162 61
## probabilities: 0.726 0.274
## left son=6616 (48 obs) right son=6617 (175 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.424625 to the right, improve=1.397327, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.438472, (0 missing)
##
## Node number 3309: 51 observations, complexity param=0.001112595
## predicted class=N expected loss=0.4117647 P(node) =0.01139665
## class counts: 30 21
## probabilities: 0.588 0.412
## left son=6618 (36 obs) right son=6619 (15 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4564545 to the right, improve=1.505882, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.122549, (0 missing)
##
## Node number 3310: 61 observations, complexity param=0.001112595
## predicted class=N expected loss=0.3278689 P(node) =0.01363128
## class counts: 41 20
## probabilities: 0.672 0.328
## left son=6620 (34 obs) right son=6621 (27 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.3263086 to the right, improve=1.316618, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.437194, (0 missing)
##
## Node number 3311: 42 observations, complexity param=0.001112595
## predicted class=Y expected loss=0.4761905 P(node) =0.009385475
## class counts: 20 22
## probabilities: 0.476 0.524
## left son=6622 (21 obs) right son=6623 (21 obs)
## Primary splits:
## A.nuppr.log < 0.8958797 to the right, improve=1.7142860, (0 missing)
## A.ratio.sum.TfIdf.nwrds < 0.35021 to the left, improve=0.2370868, (0 missing)
## Surrogate splits:
## A.ratio.sum.TfIdf.nwrds < 0.3516445 to the right, agree=0.619, adj=0.238, (0 split)
##
## Node number 6602: 25 observations
## predicted class=N expected loss=0.28 P(node) =0.005586592
## class counts: 18 7
## probabilities: 0.720 0.280
##
## Node number 6603: 9 observations
## predicted class=Y expected loss=0.4444444 P(node) =0.002011173
## class counts: 4 5
## probabilities: 0.444 0.556
##
## Node number 6616: 48 observations
## predicted class=N expected loss=0.1666667 P(node) =0.01072626
## class counts: 40 8
## probabilities: 0.833 0.167
##
## Node number 6617: 175 observations, complexity param=0.001112595
## predicted class=N expected loss=0.3028571 P(node) =0.03910615
## class counts: 122 53
## probabilities: 0.697 0.303
## left son=13234 (14 obs) right son=13235 (161 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.3611197 to the left, improve=1.6300620, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.3137872, (0 missing)
##
## Node number 6618: 36 observations
## predicted class=N expected loss=0.3333333 P(node) =0.008044693
## class counts: 24 12
## probabilities: 0.667 0.333
##
## Node number 6619: 15 observations
## predicted class=Y expected loss=0.4 P(node) =0.003351955
## class counts: 6 9
## probabilities: 0.400 0.600
##
## Node number 6620: 34 observations
## predicted class=N expected loss=0.2352941 P(node) =0.007597765
## class counts: 26 8
## probabilities: 0.765 0.235
##
## Node number 6621: 27 observations, complexity param=0.001112595
## predicted class=N expected loss=0.4444444 P(node) =0.00603352
## class counts: 15 12
## probabilities: 0.556 0.444
## left son=13242 (19 obs) right son=13243 (8 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.324545 to the left, improve=0.74122810, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.01465201, (0 missing)
##
## Node number 6622: 21 observations
## predicted class=N expected loss=0.3809524 P(node) =0.004692737
## class counts: 13 8
## probabilities: 0.619 0.381
##
## Node number 6623: 21 observations
## predicted class=Y expected loss=0.3333333 P(node) =0.004692737
## class counts: 7 14
## probabilities: 0.333 0.667
##
## Node number 13234: 14 observations
## predicted class=N expected loss=0.07142857 P(node) =0.003128492
## class counts: 13 1
## probabilities: 0.929 0.071
##
## Node number 13235: 161 observations, complexity param=0.001112595
## predicted class=N expected loss=0.3229814 P(node) =0.03597765
## class counts: 109 52
## probabilities: 0.677 0.323
## left son=26470 (115 obs) right son=26471 (46 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4086894 to the left, improve=1.0447200, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.4663481, (0 missing)
##
## Node number 13242: 19 observations
## predicted class=N expected loss=0.3684211 P(node) =0.00424581
## class counts: 12 7
## probabilities: 0.632 0.368
##
## Node number 13243: 8 observations
## predicted class=Y expected loss=0.375 P(node) =0.001787709
## class counts: 3 5
## probabilities: 0.375 0.625
##
## Node number 26470: 115 observations, complexity param=0.001112595
## predicted class=N expected loss=0.2869565 P(node) =0.02569832
## class counts: 82 33
## probabilities: 0.713 0.287
## left son=52940 (37 obs) right son=52941 (78 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.3942655 to the right, improve=0.5459701, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.1389746, (0 missing)
##
## Node number 26471: 46 observations, complexity param=0.001112595
## predicted class=N expected loss=0.4130435 P(node) =0.01027933
## class counts: 27 19
## probabilities: 0.587 0.413
## left son=52942 (39 obs) right son=52943 (7 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.4116411 to the right, improve=3.2567290, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.3081573, (0 missing)
##
## Node number 52940: 37 observations
## predicted class=N expected loss=0.2162162 P(node) =0.008268156
## class counts: 29 8
## probabilities: 0.784 0.216
##
## Node number 52941: 78 observations, complexity param=0.001112595
## predicted class=N expected loss=0.3205128 P(node) =0.01743017
## class counts: 53 25
## probabilities: 0.679 0.321
## left son=105882 (70 obs) right son=105883 (8 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 0.3910331 to the left, improve=1.6529300, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.1172161, (0 missing)
##
## Node number 52942: 39 observations
## predicted class=N expected loss=0.3333333 P(node) =0.008715084
## class counts: 26 13
## probabilities: 0.667 0.333
##
## Node number 52943: 7 observations
## predicted class=Y expected loss=0.1428571 P(node) =0.001564246
## class counts: 1 6
## probabilities: 0.143 0.857
##
## Node number 105882: 70 observations
## predicted class=N expected loss=0.2857143 P(node) =0.01564246
## class counts: 50 20
## probabilities: 0.714 0.286
##
## Node number 105883: 8 observations
## predicted class=Y expected loss=0.375 P(node) =0.001787709
## class counts: 3 5
## probabilities: 0.375 0.625
##
## n= 4475
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4475 749 N (0.83262570 0.16737430)
## 2) A.nuppr.log>=1.497866 2625 224 N (0.91466667 0.08533333)
## 4) A.nuppr.log>=2.012676 1049 57 N (0.94566254 0.05433746) *
## 5) A.nuppr.log< 2.012676 1576 167 N (0.89403553 0.10596447)
## 10) A.ratio.sum.TfIdf.nwrds< 1.310269 1569 164 N (0.89547482 0.10452518)
## 20) A.ratio.sum.TfIdf.nwrds< 0.660439 1431 142 N (0.90076869 0.09923131)
## 40) A.ratio.sum.TfIdf.nwrds>=0.2666657 1119 100 N (0.91063450 0.08936550)
## 80) A.ratio.sum.TfIdf.nwrds< 0.3740436 469 32 N (0.93176972 0.06823028) *
## 81) A.ratio.sum.TfIdf.nwrds>=0.3740436 650 68 N (0.89538462 0.10461538)
## 162) A.ratio.sum.TfIdf.nwrds>=0.4375274 419 34 N (0.91885442 0.08114558) *
## 163) A.ratio.sum.TfIdf.nwrds< 0.4375274 231 34 N (0.85281385 0.14718615)
## 326) A.ratio.sum.TfIdf.nwrds< 0.4355984 224 29 N (0.87053571 0.12946429) *
## 327) A.ratio.sum.TfIdf.nwrds>=0.4355984 7 2 Y (0.28571429 0.71428571) *
## 41) A.ratio.sum.TfIdf.nwrds< 0.2666657 312 42 N (0.86538462 0.13461538)
## 82) A.ratio.sum.TfIdf.nwrds< 0.2659316 305 38 N (0.87540984 0.12459016) *
## 83) A.ratio.sum.TfIdf.nwrds>=0.2659316 7 3 Y (0.42857143 0.57142857) *
## 21) A.ratio.sum.TfIdf.nwrds>=0.660439 138 22 N (0.84057971 0.15942029) *
## 11) A.ratio.sum.TfIdf.nwrds>=1.310269 7 3 N (0.57142857 0.42857143) *
## 3) A.nuppr.log< 1.497866 1850 525 N (0.71621622 0.28378378)
## 6) A.ratio.sum.TfIdf.nwrds< 1.129992 1706 420 N (0.75381008 0.24618992)
## 12) A.ratio.sum.TfIdf.nwrds< 0.6709461 1427 309 N (0.78346181 0.21653819)
## 24) A.nuppr.log>=1.242453 496 72 N (0.85483871 0.14516129)
## 48) A.ratio.sum.TfIdf.nwrds>=0.2080528 481 67 N (0.86070686 0.13929314)
## 96) A.ratio.sum.TfIdf.nwrds>=0.5798804 31 2 N (0.93548387 0.06451613) *
## 97) A.ratio.sum.TfIdf.nwrds< 0.5798804 450 65 N (0.85555556 0.14444444)
## 194) A.ratio.sum.TfIdf.nwrds< 0.4830579 382 51 N (0.86649215 0.13350785)
## 388) A.ratio.sum.TfIdf.nwrds>=0.4603122 18 0 N (1.00000000 0.00000000) *
## 389) A.ratio.sum.TfIdf.nwrds< 0.4603122 364 51 N (0.85989011 0.14010989)
## 778) A.ratio.sum.TfIdf.nwrds< 0.4087233 309 40 N (0.87055016 0.12944984) *
## 779) A.ratio.sum.TfIdf.nwrds>=0.4087233 55 11 N (0.80000000 0.20000000)
## 1558) A.ratio.sum.TfIdf.nwrds>=0.4152543 48 7 N (0.85416667 0.14583333) *
## 1559) A.ratio.sum.TfIdf.nwrds< 0.4152543 7 3 Y (0.42857143 0.57142857) *
## 195) A.ratio.sum.TfIdf.nwrds>=0.4830579 68 14 N (0.79411765 0.20588235)
## 390) A.ratio.sum.TfIdf.nwrds>=0.5141319 44 7 N (0.84090909 0.15909091) *
## 391) A.ratio.sum.TfIdf.nwrds< 0.5141319 24 7 N (0.70833333 0.29166667)
## 782) A.ratio.sum.TfIdf.nwrds< 0.5060435 17 3 N (0.82352941 0.17647059) *
## 783) A.ratio.sum.TfIdf.nwrds>=0.5060435 7 3 Y (0.42857143 0.57142857) *
## 49) A.ratio.sum.TfIdf.nwrds< 0.2080528 15 5 N (0.66666667 0.33333333) *
## 25) A.nuppr.log< 1.242453 931 237 N (0.74543502 0.25456498)
## 50) A.ratio.sum.TfIdf.nwrds>=0.5918449 86 10 N (0.88372093 0.11627907)
## 100) A.ratio.sum.TfIdf.nwrds< 0.6126658 36 1 N (0.97222222 0.02777778) *
## 101) A.ratio.sum.TfIdf.nwrds>=0.6126658 50 9 N (0.82000000 0.18000000)
## 202) A.ratio.sum.TfIdf.nwrds>=0.6179032 42 4 N (0.90476190 0.09523810) *
## 203) A.ratio.sum.TfIdf.nwrds< 0.6179032 8 3 Y (0.37500000 0.62500000) *
## 51) A.ratio.sum.TfIdf.nwrds< 0.5918449 845 227 N (0.73136095 0.26863905)
## 102) A.ratio.sum.TfIdf.nwrds< 0.298779 209 43 N (0.79425837 0.20574163) *
## 103) A.ratio.sum.TfIdf.nwrds>=0.298779 636 184 N (0.71069182 0.28930818)
## 206) A.ratio.sum.TfIdf.nwrds>=0.3026072 625 177 N (0.71680000 0.28320000)
## 412) A.ratio.sum.TfIdf.nwrds>=0.4728175 212 48 N (0.77358491 0.22641509)
## 824) A.ratio.sum.TfIdf.nwrds< 0.4829834 27 1 N (0.96296296 0.03703704) *
## 825) A.ratio.sum.TfIdf.nwrds>=0.4829834 185 47 N (0.74594595 0.25405405)
## 1650) A.ratio.sum.TfIdf.nwrds>=0.4857374 178 42 N (0.76404494 0.23595506)
## 3300) A.ratio.sum.TfIdf.nwrds< 0.5655899 144 30 N (0.79166667 0.20833333) *
## 3301) A.ratio.sum.TfIdf.nwrds>=0.5655899 34 12 N (0.64705882 0.35294118)
## 6602) A.ratio.sum.TfIdf.nwrds>=0.5711564 25 7 N (0.72000000 0.28000000) *
## 6603) A.ratio.sum.TfIdf.nwrds< 0.5711564 9 4 Y (0.44444444 0.55555556) *
## 1651) A.ratio.sum.TfIdf.nwrds< 0.4857374 7 2 Y (0.28571429 0.71428571) *
## 413) A.ratio.sum.TfIdf.nwrds< 0.4728175 413 129 N (0.68765133 0.31234867)
## 826) A.ratio.sum.TfIdf.nwrds< 0.3165757 36 5 N (0.86111111 0.13888889) *
## 827) A.ratio.sum.TfIdf.nwrds>=0.3165757 377 124 N (0.67108753 0.32891247)
## 1654) A.ratio.sum.TfIdf.nwrds>=0.355064 274 82 N (0.70072993 0.29927007)
## 3308) A.ratio.sum.TfIdf.nwrds< 0.4448867 223 61 N (0.72645740 0.27354260)
## 6616) A.ratio.sum.TfIdf.nwrds>=0.424625 48 8 N (0.83333333 0.16666667) *
## 6617) A.ratio.sum.TfIdf.nwrds< 0.424625 175 53 N (0.69714286 0.30285714)
## 13234) A.ratio.sum.TfIdf.nwrds< 0.3611197 14 1 N (0.92857143 0.07142857) *
## 13235) A.ratio.sum.TfIdf.nwrds>=0.3611197 161 52 N (0.67701863 0.32298137)
## 26470) A.ratio.sum.TfIdf.nwrds< 0.4086894 115 33 N (0.71304348 0.28695652)
## 52940) A.ratio.sum.TfIdf.nwrds>=0.3942655 37 8 N (0.78378378 0.21621622) *
## 52941) A.ratio.sum.TfIdf.nwrds< 0.3942655 78 25 N (0.67948718 0.32051282)
## 105882) A.ratio.sum.TfIdf.nwrds< 0.3910331 70 20 N (0.71428571 0.28571429) *
## 105883) A.ratio.sum.TfIdf.nwrds>=0.3910331 8 3 Y (0.37500000 0.62500000) *
## 26471) A.ratio.sum.TfIdf.nwrds>=0.4086894 46 19 N (0.58695652 0.41304348)
## 52942) A.ratio.sum.TfIdf.nwrds>=0.4116411 39 13 N (0.66666667 0.33333333) *
## 52943) A.ratio.sum.TfIdf.nwrds< 0.4116411 7 1 Y (0.14285714 0.85714286) *
## 3309) A.ratio.sum.TfIdf.nwrds>=0.4448867 51 21 N (0.58823529 0.41176471)
## 6618) A.ratio.sum.TfIdf.nwrds>=0.4564545 36 12 N (0.66666667 0.33333333) *
## 6619) A.ratio.sum.TfIdf.nwrds< 0.4564545 15 6 Y (0.40000000 0.60000000) *
## 1655) A.ratio.sum.TfIdf.nwrds< 0.355064 103 42 N (0.59223301 0.40776699)
## 3310) A.ratio.sum.TfIdf.nwrds< 0.342641 61 20 N (0.67213115 0.32786885)
## 6620) A.ratio.sum.TfIdf.nwrds>=0.3263086 34 8 N (0.76470588 0.23529412) *
## 6621) A.ratio.sum.TfIdf.nwrds< 0.3263086 27 12 N (0.55555556 0.44444444)
## 13242) A.ratio.sum.TfIdf.nwrds< 0.324545 19 7 N (0.63157895 0.36842105) *
## 13243) A.ratio.sum.TfIdf.nwrds>=0.324545 8 3 Y (0.37500000 0.62500000) *
## 3311) A.ratio.sum.TfIdf.nwrds>=0.342641 42 20 Y (0.47619048 0.52380952)
## 6622) A.nuppr.log>=0.8958797 21 8 N (0.61904762 0.38095238) *
## 6623) A.nuppr.log< 0.8958797 21 7 Y (0.33333333 0.66666667) *
## 207) A.ratio.sum.TfIdf.nwrds< 0.3026072 11 4 Y (0.36363636 0.63636364) *
## 13) A.ratio.sum.TfIdf.nwrds>=0.6709461 279 111 N (0.60215054 0.39784946)
## 26) A.ratio.sum.TfIdf.nwrds>=1.094536 8 0 N (1.00000000 0.00000000) *
## 27) A.ratio.sum.TfIdf.nwrds< 1.094536 271 111 N (0.59040590 0.40959410)
## 54) A.ratio.sum.TfIdf.nwrds< 0.9205242 198 74 N (0.62626263 0.37373737)
## 108) A.ratio.sum.TfIdf.nwrds>=0.7487838 113 33 N (0.70796460 0.29203540)
## 216) A.ratio.sum.TfIdf.nwrds< 0.7796549 29 3 N (0.89655172 0.10344828) *
## 217) A.ratio.sum.TfIdf.nwrds>=0.7796549 84 30 N (0.64285714 0.35714286)
## 434) A.ratio.sum.TfIdf.nwrds>=0.8074537 65 19 N (0.70769231 0.29230769) *
## 435) A.ratio.sum.TfIdf.nwrds< 0.8074537 19 8 Y (0.42105263 0.57894737) *
## 109) A.ratio.sum.TfIdf.nwrds< 0.7487838 85 41 N (0.51764706 0.48235294)
## 218) A.ratio.sum.TfIdf.nwrds< 0.7088626 44 15 N (0.65909091 0.34090909)
## 436) A.nuppr.log< 1.242453 35 10 N (0.71428571 0.28571429) *
## 437) A.nuppr.log>=1.242453 9 4 Y (0.44444444 0.55555556) *
## 219) A.ratio.sum.TfIdf.nwrds>=0.7088626 41 15 Y (0.36585366 0.63414634)
## 438) A.ratio.sum.TfIdf.nwrds>=0.7198596 30 13 Y (0.43333333 0.56666667)
## 876) A.ratio.sum.TfIdf.nwrds< 0.7443823 23 11 N (0.52173913 0.47826087)
## 1752) A.nuppr.log< 0.8958797 13 4 N (0.69230769 0.30769231) *
## 1753) A.nuppr.log>=0.8958797 10 3 Y (0.30000000 0.70000000) *
## 877) A.ratio.sum.TfIdf.nwrds>=0.7443823 7 1 Y (0.14285714 0.85714286) *
## 439) A.ratio.sum.TfIdf.nwrds< 0.7198596 11 2 Y (0.18181818 0.81818182) *
## 55) A.ratio.sum.TfIdf.nwrds>=0.9205242 73 36 Y (0.49315068 0.50684932)
## 110) A.ratio.sum.TfIdf.nwrds>=0.9413812 64 29 N (0.54687500 0.45312500)
## 220) A.ratio.sum.TfIdf.nwrds< 0.9880696 26 8 N (0.69230769 0.30769231) *
## 221) A.ratio.sum.TfIdf.nwrds>=0.9880696 38 17 Y (0.44736842 0.55263158)
## 442) A.ratio.sum.TfIdf.nwrds>=0.9961147 31 15 Y (0.48387097 0.51612903)
## 884) A.ratio.sum.TfIdf.nwrds< 1.051372 17 7 N (0.58823529 0.41176471) *
## 885) A.ratio.sum.TfIdf.nwrds>=1.051372 14 5 Y (0.35714286 0.64285714) *
## 443) A.ratio.sum.TfIdf.nwrds< 0.9961147 7 2 Y (0.28571429 0.71428571) *
## 111) A.ratio.sum.TfIdf.nwrds< 0.9413812 9 1 Y (0.11111111 0.88888889) *
## 7) A.ratio.sum.TfIdf.nwrds>=1.129992 144 39 Y (0.27083333 0.72916667)
## 14) A.ratio.sum.TfIdf.nwrds< 1.389273 50 21 Y (0.42000000 0.58000000)
## 28) A.nuppr.log< 0.8958797 29 11 N (0.62068966 0.37931034)
## 56) A.ratio.sum.TfIdf.nwrds< 1.186261 8 2 N (0.75000000 0.25000000) *
## 57) A.ratio.sum.TfIdf.nwrds>=1.186261 21 9 N (0.57142857 0.42857143)
## 114) A.ratio.sum.TfIdf.nwrds>=1.261906 13 4 N (0.69230769 0.30769231) *
## 115) A.ratio.sum.TfIdf.nwrds< 1.261906 8 3 Y (0.37500000 0.62500000) *
## 29) A.nuppr.log>=0.8958797 21 3 Y (0.14285714 0.85714286) *
## 15) A.ratio.sum.TfIdf.nwrds>=1.389273 94 18 Y (0.19148936 0.80851064) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.3966570
## 3 0.2 0.4756926
## 4 0.3 0.4790047
## 5 0.4 0.4474638
## 6 0.5 0.4388889
## 7 0.6 0.3992172
## 8 0.7 0.3121636
## 9 0.8 0.2739421
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 3497
## 2 Y 441
## Popular.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 229
## 2 308
## Prediction
## Reference N Y
## N 3497 229
## Y 441 308
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.502793e-01 3.943448e-01 8.394856e-01 8.606137e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 7.238361e-04 3.590183e-16
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.3554604
## 3 0.2 0.4018476
## 4 0.3 0.3256637
## 5 0.4 0.2932790
## 6 0.5 0.2694737
## 7 0.6 0.2633929
## 8 0.7 0.2216867
## 9 0.8 0.1990050
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 1365
## 2 Y 170
## Popular.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 348
## 2 174
## Prediction
## Reference N Y
## N 1365 348
## Y 170 174
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.481770e-01 2.508040e-01 7.288307e-01 7.668134e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 7.430363e-15
## model_id model_method
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart
## feats max.nTuningRuns
## 1 A.nuppr.log, A.ratio.sum.TfIdf.nwrds 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.668 0.082 0.7768899
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.4790047 0.8502793
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8394856 0.8606137 0.3943448 0.7031303
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.4018476 0.748177
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7288307 0.7668134 0.250804if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: A.nuppr.log, A.ratio.sum.TfIdf.nwrds"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00467 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4475
##
## CP nsplit rel error
## 1 0.044058745 0 1.0000000
## 2 0.004672897 2 0.9118825
##
## Variable importance
## A.nuppr.log A.ratio.sum.TfIdf.nwrds
## 54 46
##
## Node number 1: 4475 observations, complexity param=0.04405874
## predicted class=N expected loss=0.1673743 P(node) =1
## class counts: 3726 749
## probabilities: 0.833 0.167
## left son=2 (2625 obs) right son=3 (1850 obs)
## Primary splits:
## A.nuppr.log < 1.497866 to the right, improve=85.47560, (0 missing)
## A.ratio.sum.TfIdf.nwrds < 1.155894 to the left, improve=85.14662, (0 missing)
## Surrogate splits:
## A.ratio.sum.TfIdf.nwrds < 0.6475191 to the left, agree=0.645, adj=0.142, (0 split)
##
## Node number 2: 2625 observations
## predicted class=N expected loss=0.08533333 P(node) =0.5865922
## class counts: 2401 224
## probabilities: 0.915 0.085
##
## Node number 3: 1850 observations, complexity param=0.04405874
## predicted class=N expected loss=0.2837838 P(node) =0.4134078
## class counts: 1325 525
## probabilities: 0.716 0.284
## left son=6 (1706 obs) right son=7 (144 obs)
## Primary splits:
## A.ratio.sum.TfIdf.nwrds < 1.129992 to the left, improve=61.95156, (0 missing)
## A.nuppr.log < 1.242453 to the right, improve= 8.43495, (0 missing)
##
## Node number 6: 1706 observations
## predicted class=N expected loss=0.2461899 P(node) =0.3812291
## class counts: 1286 420
## probabilities: 0.754 0.246
##
## Node number 7: 144 observations
## predicted class=Y expected loss=0.2708333 P(node) =0.03217877
## class counts: 39 105
## probabilities: 0.271 0.729
##
## n= 4475
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4475 749 N (0.83262570 0.16737430)
## 2) A.nuppr.log>=1.497866 2625 224 N (0.91466667 0.08533333) *
## 3) A.nuppr.log< 1.497866 1850 525 N (0.71621622 0.28378378)
## 6) A.ratio.sum.TfIdf.nwrds< 1.129992 1706 420 N (0.75381008 0.24618992) *
## 7) A.ratio.sum.TfIdf.nwrds>=1.129992 144 39 Y (0.27083333 0.72916667) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.4040015
## 3 0.2 0.4040015
## 4 0.3 0.2351624
## 5 0.4 0.2351624
## 6 0.5 0.2351624
## 7 0.6 0.2351624
## 8 0.7 0.2351624
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.rpart.N
## 1 N 2401
## 2 Y 224
## Popular.fctr.predict.Max.cor.Y.rpart.Y
## 1 1325
## 2 525
## Prediction
## Reference N Y
## N 2401 1325
## Y 224 525
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.538547e-01 2.175642e-01 6.397074e-01 6.678010e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 6.770814e-172
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.3979849
## 3 0.2 0.3979849
## 4 0.3 0.1818182
## 5 0.4 0.1818182
## 6 0.5 0.1818182
## 7 0.6 0.1818182
## 8 0.7 0.1818182
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.rpart.N
## 1 N 1103
## 2 Y 107
## Popular.fctr.predict.Max.cor.Y.rpart.Y
## 1 610
## 2 237
## Prediction
## Reference N Y
## N 1103 610
## Y 107 237
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.514341e-01 2.100966e-01 6.303941e-01 6.720416e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 2.027800e-78
## model_id model_method feats
## 1 Max.cor.Y.rpart rpart A.nuppr.log, A.ratio.sum.TfIdf.nwrds
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.42 0.088
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6939202 0.2 0.4040015 0.8444697
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.6397074 0.667801 0.1767566 0.6818422
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.3979849 0.6514341
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.6303941 0.6720416 0.2100966
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.002374121 0.005313439# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: A.nuppr.log, A.ratio.sum.TfIdf.nwrds"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8306 -0.5997 -0.4585 -0.3435 2.6442
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.69316 0.14980 -4.627 3.7e-06 ***
## A.nuppr.log -1.10648 0.08301 -13.329 < 2e-16 ***
## A.ratio.sum.TfIdf.nwrds 1.35785 0.12663 10.723 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 3586.8 on 4472 degrees of freedom
## AIC: 3592.8
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.28675345
## 2 0.1 0.35147831
## 3 0.2 0.41598361
## 4 0.3 0.33384615
## 5 0.4 0.27329193
## 6 0.5 0.18807339
## 7 0.6 0.14372716
## 8 0.7 0.08596713
## 9 0.8 0.05161290
## 10 0.9 0.01846966
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.glm.N
## 1 N 2929
## 2 Y 343
## Popular.fctr.predict.Max.cor.Y.glm.Y
## 1 797
## 2 406
## Prediction
## Reference N Y
## N 2929 797
## Y 343 406
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.452514e-01 2.641826e-01 7.322161e-01 7.579659e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 4.827214e-41
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.28654727
## 2 0.1 0.35336538
## 3 0.2 0.41156069
## 4 0.3 0.35862069
## 5 0.4 0.25000000
## 6 0.5 0.15104167
## 7 0.6 0.11351351
## 8 0.7 0.07756233
## 9 0.8 0.06197183
## 10 0.9 0.03428571
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.glm.N
## 1 N 1370
## 2 Y 166
## Popular.fctr.predict.Max.cor.Y.glm.Y
## 1 343
## 2 178
## Prediction
## Reference N Y
## N 1370 343
## Y 166 178
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.525523e-01 2.631116e-01 7.333089e-01 7.710732e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 6.138793e-15
## model_id model_method feats
## 1 Max.cor.Y.glm glm A.nuppr.log, A.ratio.sum.TfIdf.nwrds
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.343 0.094
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7259296 0.2 0.4159836 0.8420117
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7322161 0.7579659 0.1496077 0.7287696
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.4115607 0.7525523
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7333089 0.7710732 0.2631116 3592.781
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.003127297 0.02286637if (!is.null(glb_date_vars)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
paste0(glb_date_vars, ".day.minutes.poly.", 1:5)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}## [1] "fitting model: Max.cor.Y.TmSrs.poly.glm"
## [1] " indep_vars: A.nuppr.log, A.ratio.sum.TfIdf.nwrds, PubDate.day.minutes.poly.1, PubDate.day.minutes.poly.2, PubDate.day.minutes.poly.3, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.5"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9093 -0.5938 -0.4273 -0.2823 2.9533
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.65903 0.15235 -4.326 1.52e-05 ***
## A.nuppr.log -1.14165 0.08484 -13.456 < 2e-16 ***
## A.ratio.sum.TfIdf.nwrds 1.18559 0.12664 9.362 < 2e-16 ***
## PubDate.day.minutes.poly.1 38.82928 4.47690 8.673 < 2e-16 ***
## PubDate.day.minutes.poly.2 5.97524 3.88987 1.536 0.125
## PubDate.day.minutes.poly.3 -1.47308 3.76724 -0.391 0.696
## PubDate.day.minutes.poly.4 23.95818 4.22807 5.666 1.46e-08 ***
## PubDate.day.minutes.poly.5 -21.92390 4.22035 -5.195 2.05e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 3440.1 on 4467 degrees of freedom
## AIC: 3456.1
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.28675345
## 2 0.1 0.38374178
## 3 0.2 0.44827586
## 4 0.3 0.42907058
## 5 0.4 0.36312849
## 6 0.5 0.26423201
## 7 0.6 0.17861340
## 8 0.7 0.10173697
## 9 0.8 0.04398448
## 10 0.9 0.01846966
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.TmSrs.poly.glm.N
## 1 N 2945
## 2 Y 307
## Popular.fctr.predict.Max.cor.Y.TmSrs.poly.glm.Y
## 1 781
## 2 442
## Prediction
## Reference N Y
## N 2945 781
## Y 307 442
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.568715e-01 3.037257e-01 7.440261e-01 7.693808e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.231995e-46
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.28654727
## 2 0.1 0.37879767
## 3 0.2 0.42840779
## 4 0.3 0.39482201
## 5 0.4 0.31157895
## 6 0.5 0.21463415
## 7 0.6 0.15183246
## 8 0.7 0.08839779
## 9 0.8 0.06179775
## 10 0.9 0.02865330
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.TmSrs.poly.glm.N
## 1 N 1371
## 2 Y 157
## Popular.fctr.predict.Max.cor.Y.TmSrs.poly.glm.Y
## 1 342
## 2 187
## Prediction
## Reference N Y
## N 1371 342
## Y 157 187
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.574137e-01 2.831146e-01 7.382877e-01 7.758033e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.766249e-16
## model_id model_method
## 1 Max.cor.Y.TmSrs.poly.glm glm
## feats
## 1 A.nuppr.log, A.ratio.sum.TfIdf.nwrds, PubDate.day.minutes.poly.1, PubDate.day.minutes.poly.2, PubDate.day.minutes.poly.3, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.5
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.558 0.182
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.762276 0.2 0.4482759 0.8462566
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7440261 0.7693808 0.2086575 0.7529375
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.4284078 0.7574137
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7382877 0.7758033 0.2831146 3456.116
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.001742155 0.001064229# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.glm"
## [1] " indep_vars: A.nuppr.log, A.ratio.sum.TfIdf.nwrds, A.nuppr.log:A.ratio.sum.TfIdf.nwrds, A.nuppr.log:S.sum.TfIdf, A.nuppr.log:PubDate.day.minutes.poly.1, A.nuppr.log:A.ratio.nstopwrds.nwrds, A.nuppr.log:S.npnct19.log, A.nuppr.log:A.npnct01.log, A.nuppr.log:H.npnct06.log, A.nuppr.log:S.npnct21.log, A.nuppr.log:A.npnct24.log, A.nuppr.log:A.npnct21.log, A.nuppr.log:A.npnct07.log, A.nuppr.log:A.npnct03.log, A.nuppr.log:A.npnct02.log, A.nuppr.log:A.npnct17.log, A.nuppr.log:A.npnct14.log, A.nuppr.log:A.npnct20.log, A.nuppr.log:A.npnct06.log, A.nuppr.log:A.npnct12.log, A.nuppr.log:A.npnct28.log, A.nuppr.log:S.npnct13.log, A.nuppr.log:A.npnct04.log, A.nuppr.log:A.npnct15.log, A.nuppr.log:A.npnct11.log, A.nuppr.log:A.nstopwrds.log, A.nuppr.log:A.ndgts.log, A.nuppr.log:H.nwrds.unq.log, A.nuppr.log:H.nchrs.log, A.nuppr.log:A.nwrds.log, A.nuppr.log:A.nchrs.log, A.nuppr.log:S.nwrds.unq.log, A.nuppr.log:A.nuppr.log"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6248 -0.6151 -0.3683 -0.1613 3.3420
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) -1.072e-01 2.286e-01 -0.469
## A.nuppr.log 2.506e-03 2.057e+00 0.001
## A.ratio.sum.TfIdf.nwrds 4.416e-01 3.436e-01 1.285
## `A.nuppr.log:A.ratio.sum.TfIdf.nwrds` 1.204e+00 4.910e-01 2.452
## `A.nuppr.log:S.sum.TfIdf` 9.347e-02 3.807e-02 2.455
## `A.nuppr.log:PubDate.day.minutes.poly.1` 2.889e+01 3.221e+00 8.969
## `A.nuppr.log:A.ratio.nstopwrds.nwrds` -3.343e+00 2.529e+00 -1.322
## `A.nuppr.log:S.npnct19.log` 2.158e-01 1.390e-01 1.553
## `A.nuppr.log:A.npnct01.log` 2.047e-01 5.071e-01 0.404
## `A.nuppr.log:H.npnct06.log` 8.332e-01 2.467e-01 3.377
## `A.nuppr.log:S.npnct21.log` NA NA NA
## `A.nuppr.log:A.npnct24.log` NA NA NA
## `A.nuppr.log:A.npnct21.log` -1.030e+01 1.400e+03 -0.007
## `A.nuppr.log:A.npnct07.log` -3.194e+01 3.011e+03 -0.011
## `A.nuppr.log:A.npnct03.log` -1.056e+01 9.919e+02 -0.011
## `A.nuppr.log:A.npnct02.log` -8.515e+00 6.640e+02 -0.013
## `A.nuppr.log:A.npnct17.log` -6.874e+00 5.253e+02 -0.013
## `A.nuppr.log:A.npnct14.log` -5.773e-01 8.228e-01 -0.702
## `A.nuppr.log:A.npnct20.log` -1.396e+01 1.053e+03 -0.013
## `A.nuppr.log:A.npnct06.log` -9.435e-01 8.190e-01 -1.152
## `A.nuppr.log:A.npnct12.log` -2.165e-01 1.039e-01 -2.083
## `A.nuppr.log:A.npnct28.log` -6.155e+00 2.281e+02 -0.027
## `A.nuppr.log:S.npnct13.log` 3.047e-01 1.105e-01 2.756
## `A.nuppr.log:A.npnct04.log` -9.867e-01 4.155e-01 -2.375
## `A.nuppr.log:A.npnct15.log` -2.800e-01 2.033e-01 -1.377
## `A.nuppr.log:A.npnct11.log` -1.402e-01 6.797e-02 -2.062
## `A.nuppr.log:A.nstopwrds.log` 7.562e-01 8.423e-01 0.898
## `A.nuppr.log:A.ndgts.log` -2.919e-01 7.241e-02 -4.031
## `A.nuppr.log:H.nwrds.unq.log` -7.441e-01 1.984e-01 -3.750
## `A.nuppr.log:H.nchrs.log` 2.823e-02 1.683e-01 0.168
## `A.nuppr.log:A.nwrds.log` 2.688e+00 8.970e-01 2.997
## `A.nuppr.log:A.nchrs.log` -1.196e+00 3.787e-01 -3.160
## `A.nuppr.log:S.nwrds.unq.log` -1.652e+00 3.820e-01 -4.325
## Pr(>|z|)
## (Intercept) 0.639181
## A.nuppr.log 0.999028
## A.ratio.sum.TfIdf.nwrds 0.198643
## `A.nuppr.log:A.ratio.sum.TfIdf.nwrds` 0.014219 *
## `A.nuppr.log:S.sum.TfIdf` 0.014096 *
## `A.nuppr.log:PubDate.day.minutes.poly.1` < 2e-16 ***
## `A.nuppr.log:A.ratio.nstopwrds.nwrds` 0.186245
## `A.nuppr.log:S.npnct19.log` 0.120410
## `A.nuppr.log:A.npnct01.log` 0.686419
## `A.nuppr.log:H.npnct06.log` 0.000733 ***
## `A.nuppr.log:S.npnct21.log` NA
## `A.nuppr.log:A.npnct24.log` NA
## `A.nuppr.log:A.npnct21.log` 0.994131
## `A.nuppr.log:A.npnct07.log` 0.991537
## `A.nuppr.log:A.npnct03.log` 0.991510
## `A.nuppr.log:A.npnct02.log` 0.989768
## `A.nuppr.log:A.npnct17.log` 0.989559
## `A.nuppr.log:A.npnct14.log` 0.482896
## `A.nuppr.log:A.npnct20.log` 0.989417
## `A.nuppr.log:A.npnct06.log` 0.249300
## `A.nuppr.log:A.npnct12.log` 0.037207 *
## `A.nuppr.log:A.npnct28.log` 0.978474
## `A.nuppr.log:S.npnct13.log` 0.005851 **
## `A.nuppr.log:A.npnct04.log` 0.017560 *
## `A.nuppr.log:A.npnct15.log` 0.168448
## `A.nuppr.log:A.npnct11.log` 0.039180 *
## `A.nuppr.log:A.nstopwrds.log` 0.369292
## `A.nuppr.log:A.ndgts.log` 5.54e-05 ***
## `A.nuppr.log:H.nwrds.unq.log` 0.000177 ***
## `A.nuppr.log:H.nchrs.log` 0.866789
## `A.nuppr.log:A.nwrds.log` 0.002730 **
## `A.nuppr.log:A.nchrs.log` 0.001580 **
## `A.nuppr.log:S.nwrds.unq.log` 1.53e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 3234.4 on 4444 degrees of freedom
## AIC: 3296.4
##
## Number of Fisher Scoring iterations: 16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.28675345
## 2 0.1 0.41187500
## 3 0.2 0.46591422
## 4 0.3 0.46701847
## 5 0.4 0.39153439
## 6 0.5 0.31269675
## 7 0.6 0.22685185
## 8 0.7 0.14896215
## 9 0.8 0.09355247
## 10 0.9 0.03403141
## 11 1.0 0.00000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 3313
## 2 Y 395
## Popular.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 413
## 2 354
## Prediction
## Reference N Y
## N 3313 413
## Y 395 354
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8194413 0.3583471 0.8078525 0.8306117 0.8326257
## AccuracyPValue McnemarPValue
## 0.9909271 0.5498013
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.28654727
## 2 0.1 0.40381992
## 3 0.2 0.47943831
## 4 0.3 0.46218487
## 5 0.4 0.39223301
## 6 0.5 0.29561201
## 7 0.6 0.20759494
## 8 0.7 0.16402116
## 9 0.8 0.11924119
## 10 0.9 0.03977273
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 1299
## 2 Y 105
## Popular.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 414
## 2 239
## Prediction
## Reference N Y
## N 1299 414
## Y 105 239
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.476908e-01 3.334126e-01 7.283332e-01 7.663400e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.196637e-41
## model_id model_method
## 1 Interact.High.cor.Y.glm glm
## feats
## 1 A.nuppr.log, A.ratio.sum.TfIdf.nwrds, A.nuppr.log:A.ratio.sum.TfIdf.nwrds, A.nuppr.log:S.sum.TfIdf, A.nuppr.log:PubDate.day.minutes.poly.1, A.nuppr.log:A.ratio.nstopwrds.nwrds, A.nuppr.log:S.npnct19.log, A.nuppr.log:A.npnct01.log, A.nuppr.log:H.npnct06.log, A.nuppr.log:S.npnct21.log, A.nuppr.log:A.npnct24.log, A.nuppr.log:A.npnct21.log, A.nuppr.log:A.npnct07.log, A.nuppr.log:A.npnct03.log, A.nuppr.log:A.npnct02.log, A.nuppr.log:A.npnct17.log, A.nuppr.log:A.npnct14.log, A.nuppr.log:A.npnct20.log, A.nuppr.log:A.npnct06.log, A.nuppr.log:A.npnct12.log, A.nuppr.log:A.npnct28.log, A.nuppr.log:S.npnct13.log, A.nuppr.log:A.npnct04.log, A.nuppr.log:A.npnct15.log, A.nuppr.log:A.npnct11.log, A.nuppr.log:A.nstopwrds.log, A.nuppr.log:A.ndgts.log, A.nuppr.log:H.nwrds.unq.log, A.nuppr.log:H.nchrs.log, A.nuppr.log:A.nwrds.log, A.nuppr.log:A.nchrs.log, A.nuppr.log:S.nwrds.unq.log, A.nuppr.log:A.nuppr.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.769 0.756
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8006718 0.3 0.4670185 0.8507265
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8078525 0.8306117 0.248979 0.7921045
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.4794383 0.7476908
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7283332 0.76634 0.3334126 3296.412
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.003796999 0.009254505# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":", feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, H.npnct19.log, A.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, A.npnct01.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, H.npnct11.log, A.npnct16.log, S.npnct16.log, S.npnct08.log, A.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, H.npnct12.log, A.npnct03.log, A.npnct20.log, H.npnct02.log, S.npnct14.log, A.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, A.npnct12.log, PubDate.wkday.fctr.nonNA, A.npnct28.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, A.npnct04.log, A.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, A.npnct11.log, A.nstopwrds.log, H.ndgts.log, A.ndgts.log, H.nwrds.unq.log, A.nuppr.log, myCategory.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8528 -0.2959 -0.1216 0.0000 3.6683
##
## Coefficients: (277 not defined because of singularities)
## Estimate
## (Intercept) -2.391e+01
## A.ratio.sum.TfIdf.nwrds 1.120e+00
## WordCount.log.nonNA 1.112e+00
## H.ratio.sum.TfIdf.nwrds 2.008e-01
## myCategory.fctr.nonNA2 3.277e-01
## myCategory.fctr.nonNA3 -3.488e+00
## myCategory.fctr.nonNA4 5.583e+00
## myCategory.fctr.nonNA5 -1.677e+01
## myCategory.fctr.nonNA6 1.755e+00
## myCategory.fctr.nonNA7 -3.006e-01
## myCategory.fctr.nonNA8 5.129e+00
## myCategory.fctr.nonNA9 2.500e+00
## myCategory.fctr.nonNA10 2.508e+00
## myCategory.fctr.nonNA11 -1.428e+01
## myCategory.fctr.nonNA12 1.617e-01
## myCategory.fctr.nonNA13 2.394e+00
## myCategory.fctr.nonNA14 -1.565e+01
## myCategory.fctr.nonNA15 3.803e+00
## myCategory.fctr.nonNA16 3.952e+00
## myCategory.fctr.nonNA17 -1.580e+01
## myCategory.fctr.nonNA18 3.548e+00
## myCategory.fctr.nonNA19 3.390e-01
## myCategory.fctr.nonNA20 -5.094e-02
## PubDate.day.minutes.poly.1 1.112e+01
## H.sum.TfIdf 2.611e-02
## S.sum.TfIdf -2.233e-01
## H.npnct19.log 1.532e+00
## A.ratio.nstopwrds.nwrds 8.778e-02
## PubDate.wkend -3.337e-01
## PubDate.day.minutes.poly.4 4.545e+00
## PubDate.day.minutes.poly.2 1.398e+01
## S.npnct19.log 1.466e+00
## H.npnct08.log 1.121e+00
## PubDate.last10.log 1.021e-01
## PubDate.last1.log -4.526e-02
## H.ratio.nstopwrds.nwrds -1.890e+00
## H.npnct06.log 1.382e+00
## A.npnct01.log 1.367e+00
## PubDate.day.minutes.poly.3 1.675e+00
## H.npnct01.log -1.593e+00
## A.npnct24.log 2.126e+01
## H.npnct11.log 3.547e-01
## A.npnct16.log -3.442e-01
## S.npnct16.log NA
## S.npnct08.log 1.113e+00
## A.npnct08.log NA
## PubDate.last100.log 3.770e-03
## .rnorm -5.143e-02
## H.npnct05.log -2.377e+01
## PubDate.date.fctr.nonNA2 4.733e-02
## PubDate.date.fctr.nonNA3 -2.074e-01
## PubDate.date.fctr.nonNA4 -9.765e-02
## PubDate.date.fctr.nonNA5 1.824e-01
## PubDate.second.fctr.nonNA2 7.763e-03
## PubDate.second.fctr.nonNA3 -4.896e-02
## PubDate.second.fctr.nonNA4 -3.391e-01
## H.npnct07.log 1.042e-01
## H.npnct12.log 4.865e-01
## A.npnct03.log -2.864e+01
## A.npnct20.log -2.420e+01
## H.npnct02.log -1.746e+01
## S.npnct14.log 9.684e+00
## A.npnct06.log -7.191e-01
## A.npnct14.log -8.938e+00
## H.npnct13.log 3.009e-02
## PubDate.minute.fctr.nonNA2 -8.111e-02
## PubDate.minute.fctr.nonNA3 -1.184e-01
## PubDate.minute.fctr.nonNA4 1.100e-01
## A.npnct12.log -5.979e-02
## PubDate.wkday.fctr.nonNA2 -6.068e-01
## PubDate.wkday.fctr.nonNA3 -1.246e+00
## PubDate.wkday.fctr.nonNA4 -7.677e-01
## PubDate.wkday.fctr.nonNA5 -1.098e+00
## PubDate.wkday.fctr.nonNA6 -9.376e-01
## PubDate.wkday.fctr.nonNA7 -9.793e-01
## A.npnct28.log -1.324e+01
## H.npnct04.log -2.293e+00
## S.npnct13.log 6.836e-01
## PubDate.day.minutes.poly.5 -8.343e+00
## H.npnct14.log -2.101e+01
## A.npnct04.log -1.223e+00
## A.npnct15.log -1.500e-01
## H.npnct15.log -9.442e-01
## H.nstopwrds.log 3.123e-01
## H.npnct28.log -1.673e-01
## A.npnct11.log -2.696e-01
## A.nstopwrds.log 5.036e-01
## H.ndgts.log 4.846e-01
## A.ndgts.log -2.410e-01
## H.nwrds.unq.log -6.708e-01
## A.nuppr.log -4.373e-01
## `myCategory.fctr##:.clusterid.fctr1` 2.053e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` -1.624e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` -3.209e-02
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` -1.011e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr1` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` NA
## `myCategory.fctrmyOther:.clusterid.fctr1` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 1.538e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 5.816e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` 3.635e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` NA
## `myCategory.fctrTStyle##:.clusterid.fctr1` NA
## `myCategory.fctr##:.clusterid.fctr2` 1.055e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` -1.492e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` -7.666e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -2.004e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 8.126e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 2.783e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 6.761e-02
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 1.803e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -7.589e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` -1.041e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -1.773e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 1.038e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.250e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -8.486e-02
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 1.694e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` -4.482e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` -1.737e-02
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` -6.730e-02
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 1.171e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr4` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 1.468e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 4.710e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -1.149e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` -6.785e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 4.242e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 1.641e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -6.453e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -1.730e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -1.716e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 1.255e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 9.962e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 4.340e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` -3.392e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.458e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 2.806e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` -3.407e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -1.708e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -1.428e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 5.262e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -1.729e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` -2.663e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 6.201e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -1.759e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 1.596e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -1.741e+01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` -2.916e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 1.330e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` -2.709e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` -8.310e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 6.681e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -6.237e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` -1.725e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 6.527e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` -1.470e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` -1.760e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 1.664e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 2.942e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` -1.726e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 1.496e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## Std. Error
## (Intercept) 3.166e+03
## A.ratio.sum.TfIdf.nwrds 4.189e-01
## WordCount.log.nonNA 9.500e-02
## H.ratio.sum.TfIdf.nwrds 1.612e-01
## myCategory.fctr.nonNA2 1.487e+00
## myCategory.fctr.nonNA3 1.640e+00
## myCategory.fctr.nonNA4 1.692e+00
## myCategory.fctr.nonNA5 6.205e+02
## myCategory.fctr.nonNA6 1.717e+00
## myCategory.fctr.nonNA7 1.398e+00
## myCategory.fctr.nonNA8 1.367e+00
## myCategory.fctr.nonNA9 1.451e+00
## myCategory.fctr.nonNA10 1.742e+00
## myCategory.fctr.nonNA11 8.909e+02
## myCategory.fctr.nonNA12 1.416e+00
## myCategory.fctr.nonNA13 1.336e+00
## myCategory.fctr.nonNA14 1.925e+03
## myCategory.fctr.nonNA15 1.475e+00
## myCategory.fctr.nonNA16 1.360e+00
## myCategory.fctr.nonNA17 1.139e+03
## myCategory.fctr.nonNA18 1.457e+00
## myCategory.fctr.nonNA19 1.639e+00
## myCategory.fctr.nonNA20 1.349e+00
## PubDate.day.minutes.poly.1 7.500e+00
## H.sum.TfIdf 7.142e-02
## S.sum.TfIdf 9.136e-02
## H.npnct19.log 3.316e-01
## A.ratio.nstopwrds.nwrds 1.456e+00
## PubDate.wkend 4.769e-01
## PubDate.day.minutes.poly.4 6.905e+00
## PubDate.day.minutes.poly.2 7.254e+00
## S.npnct19.log 3.327e-01
## H.npnct08.log 4.831e-01
## PubDate.last10.log 1.351e-01
## PubDate.last1.log 4.539e-02
## H.ratio.nstopwrds.nwrds 1.918e+00
## H.npnct06.log 5.502e-01
## A.npnct01.log 1.898e+00
## PubDate.day.minutes.poly.3 6.682e+00
## H.npnct01.log 1.347e+00
## A.npnct24.log 4.568e+03
## H.npnct11.log 2.109e-01
## A.npnct16.log 1.298e+00
## S.npnct16.log NA
## S.npnct08.log 6.115e-01
## A.npnct08.log NA
## PubDate.last100.log 4.587e-02
## .rnorm 6.383e-02
## H.npnct05.log 6.089e+03
## PubDate.date.fctr.nonNA2 2.005e-01
## PubDate.date.fctr.nonNA3 1.969e-01
## PubDate.date.fctr.nonNA4 1.944e-01
## PubDate.date.fctr.nonNA5 2.090e-01
## PubDate.second.fctr.nonNA2 1.769e-01
## PubDate.second.fctr.nonNA3 1.739e-01
## PubDate.second.fctr.nonNA4 1.804e-01
## H.npnct07.log 2.053e-01
## H.npnct12.log 3.313e-01
## A.npnct03.log 5.348e+03
## A.npnct20.log 4.690e+03
## H.npnct02.log 3.086e+03
## S.npnct14.log 2.136e+03
## A.npnct06.log 1.636e+00
## A.npnct14.log 2.136e+03
## H.npnct13.log 1.906e-01
## PubDate.minute.fctr.nonNA2 1.874e-01
## PubDate.minute.fctr.nonNA3 1.807e-01
## PubDate.minute.fctr.nonNA4 1.874e-01
## A.npnct12.log 2.024e-01
## PubDate.wkday.fctr.nonNA2 5.608e-01
## PubDate.wkday.fctr.nonNA3 6.125e-01
## PubDate.wkday.fctr.nonNA4 6.017e-01
## PubDate.wkday.fctr.nonNA5 5.975e-01
## PubDate.wkday.fctr.nonNA6 6.010e-01
## PubDate.wkday.fctr.nonNA7 4.970e-01
## A.npnct28.log 1.652e+03
## H.npnct04.log 1.056e+00
## S.npnct13.log 2.632e-01
## PubDate.day.minutes.poly.5 6.140e+00
## H.npnct14.log 1.462e+03
## A.npnct04.log 6.813e-01
## A.npnct15.log 4.880e-01
## H.npnct15.log 3.123e-01
## H.nstopwrds.log 4.173e-01
## H.npnct28.log 1.771e+00
## A.npnct11.log 1.487e-01
## A.nstopwrds.log 3.651e-01
## H.ndgts.log 2.508e-01
## A.ndgts.log 1.559e-01
## H.nwrds.unq.log 3.682e-01
## A.nuppr.log 1.536e-01
## `myCategory.fctr##:.clusterid.fctr1` 1.294e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` 1.165e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` 7.804e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` 1.299e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr1` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` NA
## `myCategory.fctrmyOther:.clusterid.fctr1` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 7.794e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 5.747e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` 7.915e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` NA
## `myCategory.fctrTStyle##:.clusterid.fctr1` NA
## `myCategory.fctr##:.clusterid.fctr2` 1.429e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 1.218e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 9.027e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 1.610e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 8.454e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 6.792e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 8.233e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 1.326e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 1.268e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 8.080e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 1.658e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 8.454e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 6.581e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 8.432e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 1.655e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 1.360e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 8.121e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.302e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 9.100e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 1.430e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 1.241e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 1.050e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 1.573e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 8.922e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 1.359e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 1.381e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 2.548e+03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.678e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 9.887e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 1.413e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 1.309e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 1.579e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.093e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 1.346e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.308e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 2.047e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` 1.282e+03
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 1.675e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 2.203e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 1.651e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 1.256e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 2.105e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 1.408e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.966e+03
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 1.591e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 1.659e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 1.386e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.608e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 1.548e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 1.384e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 2.684e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 1.668e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 2.897e+03
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 3.021e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 1.513e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.430e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 2.724e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 1.715e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## z value
## (Intercept) -0.008
## A.ratio.sum.TfIdf.nwrds 2.673
## WordCount.log.nonNA 11.703
## H.ratio.sum.TfIdf.nwrds 1.246
## myCategory.fctr.nonNA2 0.220
## myCategory.fctr.nonNA3 -2.126
## myCategory.fctr.nonNA4 3.300
## myCategory.fctr.nonNA5 -0.027
## myCategory.fctr.nonNA6 1.022
## myCategory.fctr.nonNA7 -0.215
## myCategory.fctr.nonNA8 3.753
## myCategory.fctr.nonNA9 1.723
## myCategory.fctr.nonNA10 1.440
## myCategory.fctr.nonNA11 -0.016
## myCategory.fctr.nonNA12 0.114
## myCategory.fctr.nonNA13 1.792
## myCategory.fctr.nonNA14 -0.008
## myCategory.fctr.nonNA15 2.578
## myCategory.fctr.nonNA16 2.906
## myCategory.fctr.nonNA17 -0.014
## myCategory.fctr.nonNA18 2.434
## myCategory.fctr.nonNA19 0.207
## myCategory.fctr.nonNA20 -0.038
## PubDate.day.minutes.poly.1 1.482
## H.sum.TfIdf 0.366
## S.sum.TfIdf -2.444
## H.npnct19.log 4.620
## A.ratio.nstopwrds.nwrds 0.060
## PubDate.wkend -0.700
## PubDate.day.minutes.poly.4 0.658
## PubDate.day.minutes.poly.2 1.927
## S.npnct19.log 4.405
## H.npnct08.log 2.321
## PubDate.last10.log 0.756
## PubDate.last1.log -0.997
## H.ratio.nstopwrds.nwrds -0.985
## H.npnct06.log 2.512
## A.npnct01.log 0.720
## PubDate.day.minutes.poly.3 0.251
## H.npnct01.log -1.183
## A.npnct24.log 0.005
## H.npnct11.log 1.682
## A.npnct16.log -0.265
## S.npnct16.log NA
## S.npnct08.log 1.821
## A.npnct08.log NA
## PubDate.last100.log 0.082
## .rnorm -0.806
## H.npnct05.log -0.004
## PubDate.date.fctr.nonNA2 0.236
## PubDate.date.fctr.nonNA3 -1.053
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## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` -0.006
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 0.872
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## Pr(>|z|)
## (Intercept) 0.993975
## A.ratio.sum.TfIdf.nwrds 0.007508
## WordCount.log.nonNA < 2e-16
## H.ratio.sum.TfIdf.nwrds 0.212916
## myCategory.fctr.nonNA2 0.825613
## myCategory.fctr.nonNA3 0.033509
## myCategory.fctr.nonNA4 0.000967
## myCategory.fctr.nonNA5 0.978441
## myCategory.fctr.nonNA6 0.306661
## myCategory.fctr.nonNA7 0.829703
## myCategory.fctr.nonNA8 0.000175
## myCategory.fctr.nonNA9 0.084933
## myCategory.fctr.nonNA10 0.149995
## myCategory.fctr.nonNA11 0.987216
## myCategory.fctr.nonNA12 0.909030
## myCategory.fctr.nonNA13 0.073081
## myCategory.fctr.nonNA14 0.993513
## myCategory.fctr.nonNA15 0.009942
## myCategory.fctr.nonNA16 0.003657
## myCategory.fctr.nonNA17 0.988933
## myCategory.fctr.nonNA18 0.014919
## myCategory.fctr.nonNA19 0.836153
## myCategory.fctr.nonNA20 0.969880
## PubDate.day.minutes.poly.1 0.138279
## H.sum.TfIdf 0.714700
## S.sum.TfIdf 0.014509
## H.npnct19.log 3.83e-06
## A.ratio.nstopwrds.nwrds 0.951925
## PubDate.wkend 0.484074
## PubDate.day.minutes.poly.4 0.510371
## PubDate.day.minutes.poly.2 0.053949
## S.npnct19.log 1.06e-05
## H.npnct08.log 0.020312
## PubDate.last10.log 0.449764
## PubDate.last1.log 0.318786
## H.ratio.nstopwrds.nwrds 0.324401
## H.npnct06.log 0.012009
## A.npnct01.log 0.471504
## PubDate.day.minutes.poly.3 0.802022
## H.npnct01.log 0.236820
## A.npnct24.log 0.996286
## H.npnct11.log 0.092605
## A.npnct16.log 0.790789
## S.npnct16.log NA
## S.npnct08.log 0.068663
## A.npnct08.log NA
## PubDate.last100.log 0.934506
## .rnorm 0.420393
## H.npnct05.log 0.996885
## PubDate.date.fctr.nonNA2 0.813387
## PubDate.date.fctr.nonNA3 0.292392
## PubDate.date.fctr.nonNA4 0.615439
## PubDate.date.fctr.nonNA5 0.382749
## PubDate.second.fctr.nonNA2 0.964997
## PubDate.second.fctr.nonNA3 0.778329
## PubDate.second.fctr.nonNA4 0.060137
## H.npnct07.log 0.611938
## H.npnct12.log 0.141909
## A.npnct03.log 0.995726
## A.npnct20.log 0.995883
## H.npnct02.log 0.995485
## S.npnct14.log 0.996383
## A.npnct06.log 0.660196
## A.npnct14.log 0.996662
## H.npnct13.log 0.874611
## PubDate.minute.fctr.nonNA2 0.665085
## PubDate.minute.fctr.nonNA3 0.512346
## PubDate.minute.fctr.nonNA4 0.557396
## A.npnct12.log 0.767640
## PubDate.wkday.fctr.nonNA2 0.279238
## PubDate.wkday.fctr.nonNA3 0.041958
## PubDate.wkday.fctr.nonNA4 0.202040
## PubDate.wkday.fctr.nonNA5 0.066137
## PubDate.wkday.fctr.nonNA6 0.118705
## PubDate.wkday.fctr.nonNA7 0.048780
## A.npnct28.log 0.993603
## H.npnct04.log 0.029959
## S.npnct13.log 0.009409
## PubDate.day.minutes.poly.5 0.174221
## H.npnct14.log 0.988538
## A.npnct04.log 0.072568
## A.npnct15.log 0.758533
## H.npnct15.log 0.002500
## H.nstopwrds.log 0.454243
## H.npnct28.log 0.924753
## A.npnct11.log 0.069727
## A.nstopwrds.log 0.167696
## H.ndgts.log 0.053317
## A.ndgts.log 0.122045
## H.nwrds.unq.log 0.068514
## A.nuppr.log 0.004422
## `myCategory.fctr##:.clusterid.fctr1` 0.112743
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` 0.889107
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` 0.967197
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` 0.436135
## `myCategory.fctrForeign#World#:.clusterid.fctr1` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` NA
## `myCategory.fctrmyOther:.clusterid.fctr1` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 0.048448
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 0.311542
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` 0.646016
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` NA
## `myCategory.fctrTStyle##:.clusterid.fctr1` NA
## `myCategory.fctr##:.clusterid.fctr2` 0.941177
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.990224
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.395800
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.213318
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.336470
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.681972
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.934553
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 0.173902
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 0.549423
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.897454
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 0.991466
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.219420
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.849307
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 0.919839
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 0.918478
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 0.741813
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 0.982940
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 0.958762
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 0.198336
## `myCategory.fctrScience#Health#:.clusterid.fctr4` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 0.304602
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 0.704300
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 0.273794
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 0.666178
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 0.634488
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 0.227204
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 0.640398
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 0.994582
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 0.991841
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 0.204342
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 0.480838
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.973553
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 0.829938
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 0.182163
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 0.037048
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 0.794559
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 0.993342
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` 0.991115
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.753396
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 0.993738
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 0.871832
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.621431
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 0.993332
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 0.257244
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 0.992934
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 0.854600
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 0.422644
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 0.845111
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 0.605245
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 0.665996
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 0.652176
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 0.994873
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 0.695631
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.999595
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 0.995353
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 0.271536
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.983587
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 0.994943
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 0.383105
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
##
## (Intercept)
## A.ratio.sum.TfIdf.nwrds **
## WordCount.log.nonNA ***
## H.ratio.sum.TfIdf.nwrds
## myCategory.fctr.nonNA2
## myCategory.fctr.nonNA3 *
## myCategory.fctr.nonNA4 ***
## myCategory.fctr.nonNA5
## myCategory.fctr.nonNA6
## myCategory.fctr.nonNA7
## myCategory.fctr.nonNA8 ***
## myCategory.fctr.nonNA9 .
## myCategory.fctr.nonNA10
## myCategory.fctr.nonNA11
## myCategory.fctr.nonNA12
## myCategory.fctr.nonNA13 .
## myCategory.fctr.nonNA14
## myCategory.fctr.nonNA15 **
## myCategory.fctr.nonNA16 **
## myCategory.fctr.nonNA17
## myCategory.fctr.nonNA18 *
## myCategory.fctr.nonNA19
## myCategory.fctr.nonNA20
## PubDate.day.minutes.poly.1
## H.sum.TfIdf
## S.sum.TfIdf *
## H.npnct19.log ***
## A.ratio.nstopwrds.nwrds
## PubDate.wkend
## PubDate.day.minutes.poly.4
## PubDate.day.minutes.poly.2 .
## S.npnct19.log ***
## H.npnct08.log *
## PubDate.last10.log
## PubDate.last1.log
## H.ratio.nstopwrds.nwrds
## H.npnct06.log *
## A.npnct01.log
## PubDate.day.minutes.poly.3
## H.npnct01.log
## A.npnct24.log
## H.npnct11.log .
## A.npnct16.log
## S.npnct16.log
## S.npnct08.log .
## A.npnct08.log
## PubDate.last100.log
## .rnorm
## H.npnct05.log
## PubDate.date.fctr.nonNA2
## PubDate.date.fctr.nonNA3
## PubDate.date.fctr.nonNA4
## PubDate.date.fctr.nonNA5
## PubDate.second.fctr.nonNA2
## PubDate.second.fctr.nonNA3
## PubDate.second.fctr.nonNA4 .
## H.npnct07.log
## H.npnct12.log
## A.npnct03.log
## A.npnct20.log
## H.npnct02.log
## S.npnct14.log
## A.npnct06.log
## A.npnct14.log
## H.npnct13.log
## PubDate.minute.fctr.nonNA2
## PubDate.minute.fctr.nonNA3
## PubDate.minute.fctr.nonNA4
## A.npnct12.log
## PubDate.wkday.fctr.nonNA2
## PubDate.wkday.fctr.nonNA3 *
## PubDate.wkday.fctr.nonNA4
## PubDate.wkday.fctr.nonNA5 .
## PubDate.wkday.fctr.nonNA6
## PubDate.wkday.fctr.nonNA7 *
## A.npnct28.log
## H.npnct04.log *
## S.npnct13.log **
## PubDate.day.minutes.poly.5
## H.npnct14.log
## A.npnct04.log .
## A.npnct15.log
## H.npnct15.log **
## H.nstopwrds.log
## H.npnct28.log
## A.npnct11.log .
## A.nstopwrds.log
## H.ndgts.log .
## A.ndgts.log
## H.nwrds.unq.log .
## A.nuppr.log **
## `myCategory.fctr##:.clusterid.fctr1`
## `myCategory.fctr#Multimedia#:.clusterid.fctr1`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1`
## `myCategory.fctrForeign#World#:.clusterid.fctr1`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1`
## `myCategory.fctrmyOther:.clusterid.fctr1`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` *
## `myCategory.fctrScience#Health#:.clusterid.fctr1`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1`
## `myCategory.fctrTStyle##:.clusterid.fctr1`
## `myCategory.fctr##:.clusterid.fctr2`
## `myCategory.fctr#Multimedia#:.clusterid.fctr2`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2`
## `myCategory.fctrForeign#World#:.clusterid.fctr2`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2`
## `myCategory.fctrmyOther:.clusterid.fctr2`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2`
## `myCategory.fctrScience#Health#:.clusterid.fctr2`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2`
## `myCategory.fctrTStyle##:.clusterid.fctr2`
## `myCategory.fctr##:.clusterid.fctr3`
## `myCategory.fctr#Multimedia#:.clusterid.fctr3`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3`
## `myCategory.fctrForeign#World#:.clusterid.fctr3`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3`
## `myCategory.fctrmyOther:.clusterid.fctr3`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3`
## `myCategory.fctrScience#Health#:.clusterid.fctr3`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3`
## `myCategory.fctrTStyle##:.clusterid.fctr3`
## `myCategory.fctr##:.clusterid.fctr4`
## `myCategory.fctr#Multimedia#:.clusterid.fctr4`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4`
## `myCategory.fctrForeign#World#:.clusterid.fctr4`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4`
## `myCategory.fctrmyOther:.clusterid.fctr4`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4`
## `myCategory.fctrScience#Health#:.clusterid.fctr4`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4`
## `myCategory.fctrTStyle##:.clusterid.fctr4`
## `myCategory.fctr##:.clusterid.fctr5`
## `myCategory.fctr#Multimedia#:.clusterid.fctr5`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5`
## `myCategory.fctrForeign#World#:.clusterid.fctr5`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5`
## `myCategory.fctrmyOther:.clusterid.fctr5`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5`
## `myCategory.fctrScience#Health#:.clusterid.fctr5`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5`
## `myCategory.fctrTStyle##:.clusterid.fctr5`
## `myCategory.fctr##:.clusterid.fctr6`
## `myCategory.fctr#Multimedia#:.clusterid.fctr6`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6`
## `myCategory.fctrForeign#World#:.clusterid.fctr6`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6`
## `myCategory.fctrmyOther:.clusterid.fctr6`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6`
## `myCategory.fctrScience#Health#:.clusterid.fctr6`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6`
## `myCategory.fctrTStyle##:.clusterid.fctr6`
## `myCategory.fctr##:.clusterid.fctr7`
## `myCategory.fctr#Multimedia#:.clusterid.fctr7`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7`
## `myCategory.fctrForeign#World#:.clusterid.fctr7`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7`
## `myCategory.fctrmyOther:.clusterid.fctr7`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7`
## `myCategory.fctrScience#Health#:.clusterid.fctr7`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7`
## `myCategory.fctrTStyle##:.clusterid.fctr7`
## `myCategory.fctr##:.clusterid.fctr8` *
## `myCategory.fctr#Multimedia#:.clusterid.fctr8`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8`
## `myCategory.fctrForeign#World#:.clusterid.fctr8`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8`
## `myCategory.fctrmyOther:.clusterid.fctr8`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8`
## `myCategory.fctrScience#Health#:.clusterid.fctr8`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8`
## `myCategory.fctrTStyle##:.clusterid.fctr8`
## `myCategory.fctr##:.clusterid.fctr9`
## `myCategory.fctr#Multimedia#:.clusterid.fctr9`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9`
## `myCategory.fctrForeign#World#:.clusterid.fctr9`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9`
## `myCategory.fctrmyOther:.clusterid.fctr9`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9`
## `myCategory.fctrScience#Health#:.clusterid.fctr9`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9`
## `myCategory.fctrTStyle##:.clusterid.fctr9`
## `myCategory.fctr##:.clusterid.fctr10`
## `myCategory.fctr#Multimedia#:.clusterid.fctr10`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10`
## `myCategory.fctrForeign#World#:.clusterid.fctr10`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10`
## `myCategory.fctrmyOther:.clusterid.fctr10`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10`
## `myCategory.fctrScience#Health#:.clusterid.fctr10`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10`
## `myCategory.fctrTStyle##:.clusterid.fctr10`
## `myCategory.fctr##:.clusterid.fctr11`
## `myCategory.fctr#Multimedia#:.clusterid.fctr11`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11`
## `myCategory.fctrForeign#World#:.clusterid.fctr11`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11`
## `myCategory.fctrmyOther:.clusterid.fctr11`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11`
## `myCategory.fctrScience#Health#:.clusterid.fctr11`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11`
## `myCategory.fctrTStyle##:.clusterid.fctr11`
## `myCategory.fctr##:.clusterid.fctr12`
## `myCategory.fctr#Multimedia#:.clusterid.fctr12`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12`
## `myCategory.fctrForeign#World#:.clusterid.fctr12`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12`
## `myCategory.fctrmyOther:.clusterid.fctr12`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12`
## `myCategory.fctrScience#Health#:.clusterid.fctr12`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12`
## `myCategory.fctrTStyle##:.clusterid.fctr12`
## `myCategory.fctr##:.clusterid.fctr13`
## `myCategory.fctr#Multimedia#:.clusterid.fctr13`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13`
## `myCategory.fctrForeign#World#:.clusterid.fctr13`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13`
## `myCategory.fctrmyOther:.clusterid.fctr13`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13`
## `myCategory.fctrScience#Health#:.clusterid.fctr13`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13`
## `myCategory.fctrTStyle##:.clusterid.fctr13`
## `myCategory.fctr##:.clusterid.fctr14`
## `myCategory.fctr#Multimedia#:.clusterid.fctr14`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14`
## `myCategory.fctrForeign#World#:.clusterid.fctr14`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14`
## `myCategory.fctrmyOther:.clusterid.fctr14`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14`
## `myCategory.fctrScience#Health#:.clusterid.fctr14`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14`
## `myCategory.fctrTStyle##:.clusterid.fctr14`
## `myCategory.fctr##:.clusterid.fctr15`
## `myCategory.fctr#Multimedia#:.clusterid.fctr15`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15`
## `myCategory.fctrForeign#World#:.clusterid.fctr15`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15`
## `myCategory.fctrmyOther:.clusterid.fctr15`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15`
## `myCategory.fctrScience#Health#:.clusterid.fctr15`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15`
## `myCategory.fctrTStyle##:.clusterid.fctr15`
## `myCategory.fctr##:.clusterid.fctr16`
## `myCategory.fctr#Multimedia#:.clusterid.fctr16`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16`
## `myCategory.fctrmyOther:.clusterid.fctr16`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16`
## `myCategory.fctrScience#Health#:.clusterid.fctr16`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16`
## `myCategory.fctrTStyle##:.clusterid.fctr16`
## `myCategory.fctr##:.clusterid.fctr17`
## `myCategory.fctr#Multimedia#:.clusterid.fctr17`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17`
## `myCategory.fctrmyOther:.clusterid.fctr17`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17`
## `myCategory.fctrScience#Health#:.clusterid.fctr17`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17`
## `myCategory.fctrTStyle##:.clusterid.fctr17`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1784.2 on 4321 degrees of freedom
## AIC: 2092.2
##
## Number of Fisher Scoring iterations: 18
##
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor myCategory.fctr.nonNA## Warning: contrasts dropped from factor PubDate.date.fctr.nonNA## Warning: contrasts dropped from factor PubDate.second.fctr.nonNA## Warning: contrasts dropped from factor PubDate.minute.fctr.nonNA## Warning: contrasts dropped from factor PubDate.wkday.fctr.nonNA## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.6682420
## 3 0.2 0.7347630
## 4 0.3 0.7425000
## 5 0.4 0.7451505
## 6 0.5 0.7313218
## 7 0.6 0.7111448
## 8 0.7 0.6600823
## 9 0.8 0.5701357
## 10 0.9 0.4140787
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Low.cor.X.glm.N
## 1 N 3537
## 2 Y 192
## Popular.fctr.predict.Low.cor.X.glm.Y
## 1 189
## 2 557
## Prediction
## Reference N Y
## N 3537 189
## Y 192 557
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.148603e-01 6.940441e-01 9.062982e-01 9.228764e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 9.523435e-58 9.183891e-01
## [1] " calling mypredict_mdl for OOB:"## Warning: contrasts dropped from factor myCategory.fctr.nonNA## Warning: contrasts dropped from factor PubDate.date.fctr.nonNA## Warning: contrasts dropped from factor PubDate.second.fctr.nonNA## Warning: contrasts dropped from factor PubDate.minute.fctr.nonNA## Warning: contrasts dropped from factor PubDate.wkday.fctr.nonNA## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.6297436
## 3 0.2 0.6950182
## 4 0.3 0.7081081
## 5 0.4 0.7057101
## 6 0.5 0.7009346
## 7 0.6 0.6938111
## 8 0.7 0.6238859
## 9 0.8 0.5518591
## 10 0.9 0.4000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Low.cor.X.glm.N
## 1 N 1579
## 2 Y 82
## Popular.fctr.predict.Low.cor.X.glm.Y
## 1 134
## 2 262
## Prediction
## Reference N Y
## N 1579 134
## Y 82 262
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.949927e-01 6.444742e-01 8.809341e-01 9.079138e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 7.448743e-16 5.202443e-04
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, H.npnct19.log, A.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, A.npnct01.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, H.npnct11.log, A.npnct16.log, S.npnct16.log, S.npnct08.log, A.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, H.npnct12.log, A.npnct03.log, A.npnct20.log, H.npnct02.log, S.npnct14.log, A.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, A.npnct12.log, PubDate.wkday.fctr.nonNA, A.npnct28.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, A.npnct04.log, A.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, A.npnct11.log, A.nstopwrds.log, H.ndgts.log, A.ndgts.log, H.nwrds.unq.log, A.nuppr.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 112.177 42.229
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9541851 0.4 0.7451505 0.8833492
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9062982 0.9228764 0.5585235 0.9281045
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7081081 0.8949927
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8809341 0.9079138 0.6444742 2092.229
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02092102 0.08123355rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 235.830 398.122 162.292
## 11 fit.models 7 1 398.122 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 406.586 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
# if (glb_is_classification && glb_is_binomial) {
# model_id_pfx <- "Conditional.X"
# # indep_vars_vctr <- setdiff(names(glb_fitobs_df), union(glb_rsp_var, glb_exclude_vars_as_features))
# indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"]
# } else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
# }
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
if (method %in% c("glm")) # for a "robust" glm model
indep_vars_vctr <- setdiff(indep_vars_vctr, c(NULL
,"A.nchrs.log" # correlated to "S.*"
,"A.ndgts.log" # correlated to "S.*"
,"A.nuppr.log" # correlated to "S.*"
,"A.npnct01.log" # identical to "S.npnct01.log"
,"A.npnct03.log" # correlated to "S.npnct03.log"
,"A.npnct04.log" # correlated to "S.npnct04.log"
,"A.npnct06.log" # identical to "S.npnct06.log"
,"A.npnct07.log" # identical to "S.npnct07.log"
,"A.npnct08.log" # correlated to "S.npnct08.log"
,"A.npnct11.log" # correlated to "S.*"
,"A.npnct12.log" # correlated to "S.*"
,"S.npnct14.log" # correlated to "A.*"
,"A.npnct15.log" # correlated to "S.npnct15.log"
,"A.npnct16.log" # correlated to "S.npnct16.log"
,"A.npnct19.log" # correlated to "S.*"
,"A.npnct20.log" # identical to "S.npnct20.log"
,"A.npnct21.log" # correlated to "S.npnct21.log"
,"A.P.daily.clip.report" # identical to "S.*"
,"S.P.daily.clip.report" # identical to "H.*"
,"A.P.http" # correlated to "A.npnct14.log"
,"A.P.fashion.week" # identical to "S.*"
,"H.P.first.draft" # correlated to "H.T.first"
,"A.P.first.draft" # identical to "S.*"
,"A.P.metropolitan.diary.colon" # identical to "S.*"
,"A.P.year.colon" # identical to "S.P.year.colon"
))
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 406.586 406.602 0.016
## 2 fit.models_1_glm 2 0 406.602 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr.nonNA, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, H.npnct11.log, S.npnct16.log, S.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, S.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, S.npnct12.log, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, A.npnct13.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, S.npnct04.log, S.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8481 -0.2871 -0.1156 0.0000 3.7530
##
## Coefficients: (276 not defined because of singularities)
## Estimate
## (Intercept) -2.244e+01
## A.ratio.sum.TfIdf.nwrds -6.192e+00
## WordCount.log.nonNA 1.119e+00
## S.ratio.sum.TfIdf.nwrds 7.519e+00
## H.ratio.sum.TfIdf.nwrds 3.538e-01
## myCategory.fctr.nonNA2 3.072e-01
## myCategory.fctr.nonNA3 -3.447e+00
## myCategory.fctr.nonNA4 5.499e+00
## myCategory.fctr.nonNA5 -1.695e+01
## myCategory.fctr.nonNA6 1.696e+00
## myCategory.fctr.nonNA7 -3.163e-01
## myCategory.fctr.nonNA8 5.146e+00
## myCategory.fctr.nonNA9 2.490e+00
## myCategory.fctr.nonNA10 2.288e+00
## myCategory.fctr.nonNA11 -1.417e+01
## myCategory.fctr.nonNA12 1.023e-01
## myCategory.fctr.nonNA13 2.312e+00
## myCategory.fctr.nonNA14 -1.581e+01
## myCategory.fctr.nonNA15 3.867e+00
## myCategory.fctr.nonNA16 3.899e+00
## myCategory.fctr.nonNA17 -1.604e+01
## myCategory.fctr.nonNA18 3.291e+00
## myCategory.fctr.nonNA19 4.246e-01
## myCategory.fctr.nonNA20 -1.697e-01
## PubDate.day.minutes.poly.1 4.227e+00
## H.sum.TfIdf -5.662e-03
## S.sum.TfIdf 1.192e+00
## A.sum.TfIdf -1.411e+00
## PubDate.hour.fctr.nonNA2 8.241e-01
## PubDate.hour.fctr.nonNA3 3.900e-01
## H.npnct19.log 1.476e+00
## A.ratio.nstopwrds.nwrds -1.821e+02
## S.ratio.nstopwrds.nwrds 1.761e+02
## PubDate.wkend -2.939e-01
## PubDate.day.minutes.poly.4 -9.757e+00
## PubDate.day.minutes.poly.2 3.457e+01
## S.npnct19.log 1.355e+00
## H.npnct08.log 1.051e+00
## PubDate.last10.log 1.090e-01
## PubDate.last1.log -5.505e-02
## H.ratio.nstopwrds.nwrds 4.704e+00
## H.npnct06.log 1.758e+00
## S.npnct01.log 1.483e+00
## H.npnct16.log -3.584e-01
## PubDate.day.minutes.poly.3 2.390e+00
## H.npnct01.log -1.235e+00
## A.npnct24.log 1.469e+01
## S.npnct24.log NA
## H.npnct11.log 2.657e-01
## S.npnct16.log -3.941e-01
## S.npnct08.log 1.126e+00
## PubDate.last100.log 1.280e-02
## .rnorm -5.529e-02
## H.npnct05.log -2.371e+01
## PubDate.date.fctr.nonNA2 4.766e-02
## PubDate.date.fctr.nonNA3 -1.939e-01
## PubDate.date.fctr.nonNA4 -9.680e-02
## PubDate.date.fctr.nonNA5 2.040e-01
## PubDate.second.fctr.nonNA2 -1.339e-03
## PubDate.second.fctr.nonNA3 -4.637e-02
## PubDate.second.fctr.nonNA4 -3.526e-01
## H.npnct07.log 1.571e-01
## S.npnct03.log -2.920e+01
## A.npnct18.log 5.533e+00
## H.npnct12.log 3.853e-01
## A.npnct02.log -1.647e+01
## A.npnct17.log -1.388e-01
## S.npnct20.log -2.468e+01
## H.npnct02.log -1.736e+01
## S.npnct06.log -1.211e+00
## A.npnct14.log 6.260e-01
## H.npnct13.log -2.075e-01
## PubDate.minute.fctr.nonNA2 -6.648e-02
## PubDate.minute.fctr.nonNA3 -7.149e-02
## PubDate.minute.fctr.nonNA4 1.214e-01
## S.npnct12.log -1.759e-01
## PubDate.wkday.fctr.nonNA2 -5.455e-01
## PubDate.wkday.fctr.nonNA3 -1.207e+00
## PubDate.wkday.fctr.nonNA4 -7.314e-01
## PubDate.wkday.fctr.nonNA5 -1.084e+00
## PubDate.wkday.fctr.nonNA6 -8.664e-01
## PubDate.wkday.fctr.nonNA7 -9.344e-01
## S.npnct28.log -2.837e+01
## A.npnct28.log 1.294e+01
## A.npnct13.log 7.451e-01
## H.npnct04.log -2.184e+00
## S.npnct13.log -7.598e-02
## PubDate.day.minutes.poly.5 -4.992e+00
## H.npnct14.log -2.117e+01
## S.npnct04.log -1.402e+00
## S.npnct15.log -8.605e-02
## H.npnct15.log -8.896e-01
## H.nstopwrds.log -9.482e-01
## H.npnct28.log -6.510e-01
## S.npnct11.log -2.586e-01
## S.nstopwrds.log -8.516e+01
## A.nstopwrds.log 8.693e+01
## H.ndgts.log 6.588e-01
## S.ndgts.log -2.677e-01
## H.nuppr.log 1.925e+00
## H.nwrds.log 8.251e-01
## H.nchrs.log -3.674e-01
## S.nwrds.log 1.015e+02
## A.nwrds.log -1.011e+02
## H.nwrds.unq.log -1.003e+00
## S.nchrs.log 3.704e-01
## A.nwrds.unq.log 6.795e+00
## S.nwrds.unq.log -8.782e+00
## S.nuppr.log -4.810e-01
## `myCategory.fctr##:.clusterid.fctr1` 1.961e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` -2.131e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` -3.028e-02
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` -8.480e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr1` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` NA
## `myCategory.fctrmyOther:.clusterid.fctr1` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 1.436e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 5.293e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` 3.965e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` NA
## `myCategory.fctrTStyle##:.clusterid.fctr1` NA
## `myCategory.fctr##:.clusterid.fctr2` 2.902e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` -3.670e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` -8.272e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -1.971e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 7.333e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 3.239e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 1.291e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 1.808e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -7.640e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` -1.731e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -1.740e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 1.013e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 2.483e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -7.098e-02
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 1.981e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` -4.004e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` -1.759e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` -6.864e-02
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 1.158e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr4` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 1.516e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 3.528e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -1.062e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` -4.283e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 4.450e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 1.637e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -7.799e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -1.739e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -1.696e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 1.184e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 9.531e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` -9.219e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` -1.906e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.380e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 2.741e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` -4.098e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -1.688e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -1.421e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 5.323e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -1.718e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` -2.483e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 5.280e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -1.721e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 1.470e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -1.744e+01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` -1.430e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 1.325e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` -3.230e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` -5.384e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 6.881e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -6.606e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` -1.708e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 3.870e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 1.301e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` -1.739e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 1.571e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 7.038e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` -1.743e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 1.159e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## Std. Error
## (Intercept) 3.138e+03
## A.ratio.sum.TfIdf.nwrds 1.204e+02
## WordCount.log.nonNA 9.697e-02
## S.ratio.sum.TfIdf.nwrds 1.203e+02
## H.ratio.sum.TfIdf.nwrds 2.251e-01
## myCategory.fctr.nonNA2 1.513e+00
## myCategory.fctr.nonNA3 1.664e+00
## myCategory.fctr.nonNA4 1.718e+00
## myCategory.fctr.nonNA5 5.979e+02
## myCategory.fctr.nonNA6 1.744e+00
## myCategory.fctr.nonNA7 1.422e+00
## myCategory.fctr.nonNA8 1.397e+00
## myCategory.fctr.nonNA9 1.471e+00
## myCategory.fctr.nonNA10 1.781e+00
## myCategory.fctr.nonNA11 8.776e+02
## myCategory.fctr.nonNA12 1.443e+00
## myCategory.fctr.nonNA13 1.362e+00
## myCategory.fctr.nonNA14 1.891e+03
## myCategory.fctr.nonNA15 1.498e+00
## myCategory.fctr.nonNA16 1.387e+00
## myCategory.fctr.nonNA17 1.117e+03
## myCategory.fctr.nonNA18 1.481e+00
## myCategory.fctr.nonNA19 1.660e+00
## myCategory.fctr.nonNA20 1.376e+00
## PubDate.day.minutes.poly.1 1.658e+01
## H.sum.TfIdf 8.005e-02
## S.sum.TfIdf 8.636e+00
## A.sum.TfIdf 8.641e+00
## PubDate.hour.fctr.nonNA2 5.008e-01
## PubDate.hour.fctr.nonNA3 5.710e-01
## H.npnct19.log 3.367e-01
## A.ratio.nstopwrds.nwrds 5.850e+02
## S.ratio.nstopwrds.nwrds 5.849e+02
## PubDate.wkend 4.815e-01
## PubDate.day.minutes.poly.4 1.032e+01
## PubDate.day.minutes.poly.2 1.272e+01
## S.npnct19.log 3.498e-01
## H.npnct08.log 4.966e-01
## PubDate.last10.log 1.378e-01
## PubDate.last1.log 4.616e-02
## H.ratio.nstopwrds.nwrds 2.699e+00
## H.npnct06.log 9.980e-01
## S.npnct01.log 2.068e+00
## H.npnct16.log 1.079e+00
## PubDate.day.minutes.poly.3 6.899e+00
## H.npnct01.log 1.310e+00
## A.npnct24.log 4.527e+03
## S.npnct24.log NA
## H.npnct11.log 2.182e-01
## S.npnct16.log 1.345e+00
## S.npnct08.log 6.179e-01
## PubDate.last100.log 4.680e-02
## .rnorm 6.472e-02
## H.npnct05.log 6.088e+03
## PubDate.date.fctr.nonNA2 2.031e-01
## PubDate.date.fctr.nonNA3 1.995e-01
## PubDate.date.fctr.nonNA4 1.971e-01
## PubDate.date.fctr.nonNA5 2.110e-01
## PubDate.second.fctr.nonNA2 1.790e-01
## PubDate.second.fctr.nonNA3 1.760e-01
## PubDate.second.fctr.nonNA4 1.827e-01
## H.npnct07.log 2.175e-01
## S.npnct03.log 5.295e+03
## A.npnct18.log 1.971e+04
## H.npnct12.log 3.345e-01
## A.npnct02.log 1.023e+04
## A.npnct17.log 6.989e+03
## S.npnct20.log 4.659e+03
## H.npnct02.log 3.094e+03
## S.npnct06.log 1.832e+00
## A.npnct14.log 1.600e+00
## H.npnct13.log 2.061e-01
## PubDate.minute.fctr.nonNA2 1.903e-01
## PubDate.minute.fctr.nonNA3 1.837e-01
## PubDate.minute.fctr.nonNA4 1.906e-01
## S.npnct12.log 2.136e-01
## PubDate.wkday.fctr.nonNA2 5.668e-01
## PubDate.wkday.fctr.nonNA3 6.184e-01
## PubDate.wkday.fctr.nonNA4 6.074e-01
## PubDate.wkday.fctr.nonNA5 6.026e-01
## PubDate.wkday.fctr.nonNA6 6.062e-01
## PubDate.wkday.fctr.nonNA7 5.073e-01
## S.npnct28.log 2.329e+04
## A.npnct28.log 2.313e+04
## A.npnct13.log 2.040e+00
## H.npnct04.log 1.061e+00
## S.npnct13.log 2.024e+00
## PubDate.day.minutes.poly.5 6.954e+00
## H.npnct14.log 1.448e+03
## S.npnct04.log 6.848e-01
## S.npnct15.log 5.033e-01
## H.npnct15.log 3.186e-01
## H.nstopwrds.log 6.138e-01
## H.npnct28.log 1.837e+00
## S.npnct11.log 1.517e-01
## S.nstopwrds.log 2.236e+02
## A.nstopwrds.log 2.237e+02
## H.ndgts.log 2.671e-01
## S.ndgts.log 1.597e-01
## H.nuppr.log 7.067e-01
## H.nwrds.log 1.058e+00
## H.nchrs.log 5.522e-01
## S.nwrds.log 2.291e+02
## A.nwrds.log 2.291e+02
## H.nwrds.unq.log 4.717e-01
## S.nchrs.log 7.972e-01
## A.nwrds.unq.log 3.907e+01
## S.nwrds.unq.log 3.906e+01
## S.nuppr.log 1.564e-01
## `myCategory.fctr##:.clusterid.fctr1` 1.321e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` 1.174e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` 7.818e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` 1.324e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr1` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` NA
## `myCategory.fctrmyOther:.clusterid.fctr1` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 7.799e-01
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## `myCategory.fctr##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## z value
## (Intercept) -0.007
## A.ratio.sum.TfIdf.nwrds -0.051
## WordCount.log.nonNA 11.541
## S.ratio.sum.TfIdf.nwrds 0.062
## H.ratio.sum.TfIdf.nwrds 1.572
## myCategory.fctr.nonNA2 0.203
## myCategory.fctr.nonNA3 -2.071
## myCategory.fctr.nonNA4 3.201
## myCategory.fctr.nonNA5 -0.028
## myCategory.fctr.nonNA6 0.973
## myCategory.fctr.nonNA7 -0.222
## myCategory.fctr.nonNA8 3.684
## myCategory.fctr.nonNA9 1.692
## myCategory.fctr.nonNA10 1.285
## myCategory.fctr.nonNA11 -0.016
## myCategory.fctr.nonNA12 0.071
## myCategory.fctr.nonNA13 1.698
## myCategory.fctr.nonNA14 -0.008
## myCategory.fctr.nonNA15 2.581
## myCategory.fctr.nonNA16 2.810
## myCategory.fctr.nonNA17 -0.014
## myCategory.fctr.nonNA18 2.222
## myCategory.fctr.nonNA19 0.256
## myCategory.fctr.nonNA20 -0.123
## PubDate.day.minutes.poly.1 0.255
## H.sum.TfIdf -0.071
## S.sum.TfIdf 0.138
## A.sum.TfIdf -0.163
## PubDate.hour.fctr.nonNA2 1.645
## PubDate.hour.fctr.nonNA3 0.683
## H.npnct19.log 4.385
## A.ratio.nstopwrds.nwrds -0.311
## S.ratio.nstopwrds.nwrds 0.301
## PubDate.wkend -0.610
## PubDate.day.minutes.poly.4 -0.945
## PubDate.day.minutes.poly.2 2.719
## S.npnct19.log 3.875
## H.npnct08.log 2.116
## PubDate.last10.log 0.791
## PubDate.last1.log -1.193
## H.ratio.nstopwrds.nwrds 1.743
## H.npnct06.log 1.761
## S.npnct01.log 0.717
## H.npnct16.log -0.332
## PubDate.day.minutes.poly.3 0.346
## H.npnct01.log -0.943
## A.npnct24.log 0.003
## S.npnct24.log NA
## H.npnct11.log 1.218
## S.npnct16.log -0.293
## S.npnct08.log 1.822
## PubDate.last100.log 0.274
## .rnorm -0.854
## H.npnct05.log -0.004
## PubDate.date.fctr.nonNA2 0.235
## PubDate.date.fctr.nonNA3 -0.972
## PubDate.date.fctr.nonNA4 -0.491
## PubDate.date.fctr.nonNA5 0.967
## PubDate.second.fctr.nonNA2 -0.007
## PubDate.second.fctr.nonNA3 -0.264
## PubDate.second.fctr.nonNA4 -1.929
## H.npnct07.log 0.722
## S.npnct03.log -0.006
## A.npnct18.log 0.000
## H.npnct12.log 1.152
## A.npnct02.log -0.002
## A.npnct17.log 0.000
## S.npnct20.log -0.005
## H.npnct02.log -0.006
## S.npnct06.log -0.661
## A.npnct14.log 0.391
## H.npnct13.log -1.007
## PubDate.minute.fctr.nonNA2 -0.349
## PubDate.minute.fctr.nonNA3 -0.389
## PubDate.minute.fctr.nonNA4 0.637
## S.npnct12.log -0.823
## PubDate.wkday.fctr.nonNA2 -0.963
## PubDate.wkday.fctr.nonNA3 -1.952
## PubDate.wkday.fctr.nonNA4 -1.204
## PubDate.wkday.fctr.nonNA5 -1.799
## PubDate.wkday.fctr.nonNA6 -1.429
## PubDate.wkday.fctr.nonNA7 -1.842
## S.npnct28.log -0.001
## A.npnct28.log 0.001
## A.npnct13.log 0.365
## H.npnct04.log -2.059
## S.npnct13.log -0.038
## PubDate.day.minutes.poly.5 -0.718
## H.npnct14.log -0.015
## S.npnct04.log -2.047
## S.npnct15.log -0.171
## H.npnct15.log -2.793
## H.nstopwrds.log -1.545
## H.npnct28.log -0.354
## S.npnct11.log -1.704
## S.nstopwrds.log -0.381
## A.nstopwrds.log 0.389
## H.ndgts.log 2.466
## S.ndgts.log -1.677
## H.nuppr.log 2.724
## H.nwrds.log 0.780
## H.nchrs.log -0.665
## S.nwrds.log 0.443
## A.nwrds.log -0.441
## H.nwrds.unq.log -2.127
## S.nchrs.log 0.465
## A.nwrds.unq.log 0.174
## S.nwrds.unq.log -0.225
## S.nuppr.log -3.076
## `myCategory.fctr##:.clusterid.fctr1` 1.485
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` -0.181
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` -0.039
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` -0.640
## `myCategory.fctrForeign#World#:.clusterid.fctr1` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` NA
## `myCategory.fctrmyOther:.clusterid.fctr1` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 1.842
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 0.910
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` 0.499
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` NA
## `myCategory.fctrTStyle##:.clusterid.fctr1` NA
## `myCategory.fctr##:.clusterid.fctr2` 0.201
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` -0.030
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` -0.908
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -1.196
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.867
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.474
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.156
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 1.338
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -0.596
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` -0.213
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -0.010
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 1.196
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.373
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -0.083
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 0.117
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` -0.292
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` -0.216
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` -0.052
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 1.264
## `myCategory.fctrScience#Health#:.clusterid.fctr4` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 1.042
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 0.281
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -1.011
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` -0.268
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 0.496
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 1.182
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -0.555
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -0.007
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -0.010
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 1.205
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 0.662
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` -0.069
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` -0.119
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.255
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 1.999
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` -0.309
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -0.008
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -0.011
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.315
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -0.008
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` -0.149
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.416
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -0.008
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 1.019
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -0.009
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` -0.089
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 0.786
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` -0.230
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` -0.330
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 0.437
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -0.473
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` -0.006
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 0.228
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.001
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` -0.006
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 0.998
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.049
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` -0.006
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 0.661
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## Pr(>|z|)
## (Intercept) 0.994293
## A.ratio.sum.TfIdf.nwrds 0.958982
## WordCount.log.nonNA < 2e-16
## S.ratio.sum.TfIdf.nwrds 0.950177
## H.ratio.sum.TfIdf.nwrds 0.115993
## myCategory.fctr.nonNA2 0.839048
## myCategory.fctr.nonNA3 0.038341
## myCategory.fctr.nonNA4 0.001371
## myCategory.fctr.nonNA5 0.977379
## myCategory.fctr.nonNA6 0.330714
## myCategory.fctr.nonNA7 0.824036
## myCategory.fctr.nonNA8 0.000229
## myCategory.fctr.nonNA9 0.090585
## myCategory.fctr.nonNA10 0.198961
## myCategory.fctr.nonNA11 0.987114
## myCategory.fctr.nonNA12 0.943478
## myCategory.fctr.nonNA13 0.089483
## myCategory.fctr.nonNA14 0.993328
## myCategory.fctr.nonNA15 0.009854
## myCategory.fctr.nonNA16 0.004951
## myCategory.fctr.nonNA17 0.988548
## myCategory.fctr.nonNA18 0.026277
## myCategory.fctr.nonNA19 0.798067
## myCategory.fctr.nonNA20 0.901858
## PubDate.day.minutes.poly.1 0.798725
## H.sum.TfIdf 0.943614
## S.sum.TfIdf 0.890184
## A.sum.TfIdf 0.870263
## PubDate.hour.fctr.nonNA2 0.099881
## PubDate.hour.fctr.nonNA3 0.494620
## H.npnct19.log 1.16e-05
## A.ratio.nstopwrds.nwrds 0.755527
## S.ratio.nstopwrds.nwrds 0.763385
## PubDate.wkend 0.541595
## PubDate.day.minutes.poly.4 0.344530
## PubDate.day.minutes.poly.2 0.006552
## S.npnct19.log 0.000107
## H.npnct08.log 0.034344
## PubDate.last10.log 0.429157
## PubDate.last1.log 0.232953
## H.ratio.nstopwrds.nwrds 0.081390
## H.npnct06.log 0.078185
## S.npnct01.log 0.473458
## H.npnct16.log 0.739741
## PubDate.day.minutes.poly.3 0.729076
## H.npnct01.log 0.345844
## A.npnct24.log 0.997411
## S.npnct24.log NA
## H.npnct11.log 0.223379
## S.npnct16.log 0.769491
## S.npnct08.log 0.068402
## PubDate.last100.log 0.784428
## .rnorm 0.392959
## H.npnct05.log 0.996892
## PubDate.date.fctr.nonNA2 0.814499
## PubDate.date.fctr.nonNA3 0.331198
## PubDate.date.fctr.nonNA4 0.623354
## PubDate.date.fctr.nonNA5 0.333600
## PubDate.second.fctr.nonNA2 0.994030
## PubDate.second.fctr.nonNA3 0.792115
## PubDate.second.fctr.nonNA4 0.053674
## H.npnct07.log 0.470094
## S.npnct03.log 0.995600
## A.npnct18.log 0.999776
## H.npnct12.log 0.249486
## A.npnct02.log 0.998715
## A.npnct17.log 0.999984
## S.npnct20.log 0.995773
## H.npnct02.log 0.995524
## S.npnct06.log 0.508511
## A.npnct14.log 0.695593
## H.npnct13.log 0.314088
## PubDate.minute.fctr.nonNA2 0.726800
## PubDate.minute.fctr.nonNA3 0.697175
## PubDate.minute.fctr.nonNA4 0.523983
## S.npnct12.log 0.410268
## PubDate.wkday.fctr.nonNA2 0.335774
## PubDate.wkday.fctr.nonNA3 0.050951
## PubDate.wkday.fctr.nonNA4 0.228493
## PubDate.wkday.fctr.nonNA5 0.072068
## PubDate.wkday.fctr.nonNA6 0.152959
## PubDate.wkday.fctr.nonNA7 0.065461
## S.npnct28.log 0.999028
## A.npnct28.log 0.999554
## A.npnct13.log 0.714933
## H.npnct04.log 0.039523
## S.npnct13.log 0.970052
## PubDate.day.minutes.poly.5 0.472780
## H.npnct14.log 0.988331
## S.npnct04.log 0.040651
## S.npnct15.log 0.864251
## H.npnct15.log 0.005230
## H.nstopwrds.log 0.122389
## H.npnct28.log 0.723051
## S.npnct11.log 0.088388
## S.nstopwrds.log 0.703343
## A.nstopwrds.log 0.697534
## H.ndgts.log 0.013658
## S.ndgts.log 0.093627
## H.nuppr.log 0.006454
## H.nwrds.log 0.435634
## H.nchrs.log 0.505857
## S.nwrds.log 0.657799
## A.nwrds.log 0.658943
## H.nwrds.unq.log 0.033461
## S.nchrs.log 0.642204
## A.nwrds.unq.log 0.861913
## S.nwrds.unq.log 0.822081
## S.nuppr.log 0.002101
## `myCategory.fctr##:.clusterid.fctr1` 0.137577
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` 0.855982
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` 0.969102
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` 0.521872
## `myCategory.fctrForeign#World#:.clusterid.fctr1` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` NA
## `myCategory.fctrmyOther:.clusterid.fctr1` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 0.065493
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 0.363013
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` 0.617755
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` NA
## `myCategory.fctrTStyle##:.clusterid.fctr1` NA
## `myCategory.fctr##:.clusterid.fctr2` 0.840995
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.976214
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.363921
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.231763
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.386075
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.635144
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.876312
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 0.180878
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 0.551196
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.831207
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 0.991660
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.231575
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.709109
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 0.933725
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 0.907101
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 0.770488
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 0.829201
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 0.958792
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 0.206160
## `myCategory.fctrScience#Health#:.clusterid.fctr4` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 0.297594
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 0.778639
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 0.311924
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 0.788772
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 0.620036
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 0.237270
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 0.578601
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 0.994426
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 0.991932
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 0.228272
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 0.507775
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.944618
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 0.905387
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 0.209617
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 0.045615
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 0.756957
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 0.993490
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` 0.991172
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.752787
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 0.993827
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 0.881673
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.677718
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 0.993490
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 0.308440
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 0.992863
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 0.929438
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 0.431778
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 0.818453
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 0.741703
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 0.662260
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 0.636200
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 0.994971
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 0.819322
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.999586
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 0.995389
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 0.318182
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.961087
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 0.994879
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 0.508636
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
##
## (Intercept)
## A.ratio.sum.TfIdf.nwrds
## WordCount.log.nonNA ***
## S.ratio.sum.TfIdf.nwrds
## H.ratio.sum.TfIdf.nwrds
## myCategory.fctr.nonNA2
## myCategory.fctr.nonNA3 *
## myCategory.fctr.nonNA4 **
## myCategory.fctr.nonNA5
## myCategory.fctr.nonNA6
## myCategory.fctr.nonNA7
## myCategory.fctr.nonNA8 ***
## myCategory.fctr.nonNA9 .
## myCategory.fctr.nonNA10
## myCategory.fctr.nonNA11
## myCategory.fctr.nonNA12
## myCategory.fctr.nonNA13 .
## myCategory.fctr.nonNA14
## myCategory.fctr.nonNA15 **
## myCategory.fctr.nonNA16 **
## myCategory.fctr.nonNA17
## myCategory.fctr.nonNA18 *
## myCategory.fctr.nonNA19
## myCategory.fctr.nonNA20
## PubDate.day.minutes.poly.1
## H.sum.TfIdf
## S.sum.TfIdf
## A.sum.TfIdf
## PubDate.hour.fctr.nonNA2 .
## PubDate.hour.fctr.nonNA3
## H.npnct19.log ***
## A.ratio.nstopwrds.nwrds
## S.ratio.nstopwrds.nwrds
## PubDate.wkend
## PubDate.day.minutes.poly.4
## PubDate.day.minutes.poly.2 **
## S.npnct19.log ***
## H.npnct08.log *
## PubDate.last10.log
## PubDate.last1.log
## H.ratio.nstopwrds.nwrds .
## H.npnct06.log .
## S.npnct01.log
## H.npnct16.log
## PubDate.day.minutes.poly.3
## H.npnct01.log
## A.npnct24.log
## S.npnct24.log
## H.npnct11.log
## S.npnct16.log
## S.npnct08.log .
## PubDate.last100.log
## .rnorm
## H.npnct05.log
## PubDate.date.fctr.nonNA2
## PubDate.date.fctr.nonNA3
## PubDate.date.fctr.nonNA4
## PubDate.date.fctr.nonNA5
## PubDate.second.fctr.nonNA2
## PubDate.second.fctr.nonNA3
## PubDate.second.fctr.nonNA4 .
## H.npnct07.log
## S.npnct03.log
## A.npnct18.log
## H.npnct12.log
## A.npnct02.log
## A.npnct17.log
## S.npnct20.log
## H.npnct02.log
## S.npnct06.log
## A.npnct14.log
## H.npnct13.log
## PubDate.minute.fctr.nonNA2
## PubDate.minute.fctr.nonNA3
## PubDate.minute.fctr.nonNA4
## S.npnct12.log
## PubDate.wkday.fctr.nonNA2
## PubDate.wkday.fctr.nonNA3 .
## PubDate.wkday.fctr.nonNA4
## PubDate.wkday.fctr.nonNA5 .
## PubDate.wkday.fctr.nonNA6
## PubDate.wkday.fctr.nonNA7 .
## S.npnct28.log
## A.npnct28.log
## A.npnct13.log
## H.npnct04.log *
## S.npnct13.log
## PubDate.day.minutes.poly.5
## H.npnct14.log
## S.npnct04.log *
## S.npnct15.log
## H.npnct15.log **
## H.nstopwrds.log
## H.npnct28.log
## S.npnct11.log .
## S.nstopwrds.log
## A.nstopwrds.log
## H.ndgts.log *
## S.ndgts.log .
## H.nuppr.log **
## H.nwrds.log
## H.nchrs.log
## S.nwrds.log
## A.nwrds.log
## H.nwrds.unq.log *
## S.nchrs.log
## A.nwrds.unq.log
## S.nwrds.unq.log
## S.nuppr.log **
## `myCategory.fctr##:.clusterid.fctr1`
## `myCategory.fctr#Multimedia#:.clusterid.fctr1`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1`
## `myCategory.fctrForeign#World#:.clusterid.fctr1`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1`
## `myCategory.fctrmyOther:.clusterid.fctr1`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` .
## `myCategory.fctrScience#Health#:.clusterid.fctr1`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1`
## `myCategory.fctrTStyle##:.clusterid.fctr1`
## `myCategory.fctr##:.clusterid.fctr2`
## `myCategory.fctr#Multimedia#:.clusterid.fctr2`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2`
## `myCategory.fctrForeign#World#:.clusterid.fctr2`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2`
## `myCategory.fctrmyOther:.clusterid.fctr2`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2`
## `myCategory.fctrScience#Health#:.clusterid.fctr2`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2`
## `myCategory.fctrTStyle##:.clusterid.fctr2`
## `myCategory.fctr##:.clusterid.fctr3`
## `myCategory.fctr#Multimedia#:.clusterid.fctr3`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3`
## `myCategory.fctrForeign#World#:.clusterid.fctr3`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3`
## `myCategory.fctrmyOther:.clusterid.fctr3`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3`
## `myCategory.fctrScience#Health#:.clusterid.fctr3`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3`
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## `myCategory.fctr##:.clusterid.fctr4`
## `myCategory.fctr#Multimedia#:.clusterid.fctr4`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4`
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4`
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## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4`
## `myCategory.fctrForeign#World#:.clusterid.fctr4`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4`
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## `myCategory.fctrScience#Health#:.clusterid.fctr4`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4`
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## `myCategory.fctr##:.clusterid.fctr5`
## `myCategory.fctr#Multimedia#:.clusterid.fctr5`
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## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5`
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## `myCategory.fctrForeign#World#:.clusterid.fctr5`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5`
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## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5`
## `myCategory.fctrScience#Health#:.clusterid.fctr5`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5`
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## `myCategory.fctr##:.clusterid.fctr6`
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## `myCategory.fctrForeign#World#:.clusterid.fctr6`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6`
## `myCategory.fctrmyOther:.clusterid.fctr6`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6`
## `myCategory.fctrScience#Health#:.clusterid.fctr6`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6`
## `myCategory.fctrTStyle##:.clusterid.fctr6`
## `myCategory.fctr##:.clusterid.fctr7`
## `myCategory.fctr#Multimedia#:.clusterid.fctr7`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7`
## `myCategory.fctrForeign#World#:.clusterid.fctr7`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7`
## `myCategory.fctrmyOther:.clusterid.fctr7`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7`
## `myCategory.fctrScience#Health#:.clusterid.fctr7`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7`
## `myCategory.fctrTStyle##:.clusterid.fctr7`
## `myCategory.fctr##:.clusterid.fctr8` *
## `myCategory.fctr#Multimedia#:.clusterid.fctr8`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8`
## `myCategory.fctrForeign#World#:.clusterid.fctr8`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8`
## `myCategory.fctrmyOther:.clusterid.fctr8`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8`
## `myCategory.fctrScience#Health#:.clusterid.fctr8`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8`
## `myCategory.fctrTStyle##:.clusterid.fctr8`
## `myCategory.fctr##:.clusterid.fctr9`
## `myCategory.fctr#Multimedia#:.clusterid.fctr9`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9`
## `myCategory.fctrForeign#World#:.clusterid.fctr9`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9`
## `myCategory.fctrmyOther:.clusterid.fctr9`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9`
## `myCategory.fctrScience#Health#:.clusterid.fctr9`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9`
## `myCategory.fctrTStyle##:.clusterid.fctr9`
## `myCategory.fctr##:.clusterid.fctr10`
## `myCategory.fctr#Multimedia#:.clusterid.fctr10`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10`
## `myCategory.fctrForeign#World#:.clusterid.fctr10`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10`
## `myCategory.fctrmyOther:.clusterid.fctr10`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10`
## `myCategory.fctrScience#Health#:.clusterid.fctr10`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10`
## `myCategory.fctrTStyle##:.clusterid.fctr10`
## `myCategory.fctr##:.clusterid.fctr11`
## `myCategory.fctr#Multimedia#:.clusterid.fctr11`
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## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11`
## `myCategory.fctrForeign#World#:.clusterid.fctr11`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11`
## `myCategory.fctrmyOther:.clusterid.fctr11`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11`
## `myCategory.fctrScience#Health#:.clusterid.fctr11`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11`
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## `myCategory.fctr#Multimedia#:.clusterid.fctr12`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12`
## `myCategory.fctrForeign#World#:.clusterid.fctr12`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12`
## `myCategory.fctrmyOther:.clusterid.fctr12`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12`
## `myCategory.fctrScience#Health#:.clusterid.fctr12`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12`
## `myCategory.fctrTStyle##:.clusterid.fctr12`
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## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13`
## `myCategory.fctrForeign#World#:.clusterid.fctr13`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13`
## `myCategory.fctrmyOther:.clusterid.fctr13`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13`
## `myCategory.fctrScience#Health#:.clusterid.fctr13`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13`
## `myCategory.fctrTStyle##:.clusterid.fctr13`
## `myCategory.fctr##:.clusterid.fctr14`
## `myCategory.fctr#Multimedia#:.clusterid.fctr14`
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## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14`
## `myCategory.fctrForeign#World#:.clusterid.fctr14`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14`
## `myCategory.fctrmyOther:.clusterid.fctr14`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14`
## `myCategory.fctrScience#Health#:.clusterid.fctr14`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14`
## `myCategory.fctrTStyle##:.clusterid.fctr14`
## `myCategory.fctr##:.clusterid.fctr15`
## `myCategory.fctr#Multimedia#:.clusterid.fctr15`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15`
## `myCategory.fctrForeign#World#:.clusterid.fctr15`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15`
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## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15`
## `myCategory.fctrScience#Health#:.clusterid.fctr15`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15`
## `myCategory.fctrTStyle##:.clusterid.fctr15`
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## `myCategory.fctr#Multimedia#:.clusterid.fctr16`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16`
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## `myCategory.fctrForeign#World#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16`
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## `myCategory.fctrStyles##Fashion:.clusterid.fctr16`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16`
## `myCategory.fctrTStyle##:.clusterid.fctr16`
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## `myCategory.fctr#Multimedia#:.clusterid.fctr17`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17`
## `myCategory.fctrmyOther:.clusterid.fctr17`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17`
## `myCategory.fctrScience#Health#:.clusterid.fctr17`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17`
## `myCategory.fctrTStyle##:.clusterid.fctr17`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1758.3 on 4302 degrees of freedom
## AIC: 2104.3
##
## Number of Fisher Scoring iterations: 18
##
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor myCategory.fctr.nonNA## Warning: contrasts dropped from factor PubDate.hour.fctr.nonNA## Warning: contrasts dropped from factor PubDate.date.fctr.nonNA## Warning: contrasts dropped from factor PubDate.second.fctr.nonNA## Warning: contrasts dropped from factor PubDate.minute.fctr.nonNA## Warning: contrasts dropped from factor PubDate.wkday.fctr.nonNA## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.6714150
## 3 0.2 0.7332205
## 4 0.3 0.7534077
## 5 0.4 0.7501666
## 6 0.5 0.7397849
## 7 0.6 0.7227273
## 8 0.7 0.6655765
## 9 0.8 0.5786164
## 10 0.9 0.4231166
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.All.X.glm.N
## 1 N 3469
## 2 Y 141
## Popular.fctr.predict.All.X.glm.Y
## 1 257
## 2 608
## Prediction
## Reference N Y
## N 3469 257
## Y 141 608
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.110615e-01 6.994962e-01 9.023390e-01 9.192430e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 3.140456e-52 8.193470e-09
## [1] " calling mypredict_mdl for OOB:"## Warning: contrasts dropped from factor myCategory.fctr.nonNA## Warning: contrasts dropped from factor PubDate.hour.fctr.nonNA## Warning: contrasts dropped from factor PubDate.date.fctr.nonNA## Warning: contrasts dropped from factor PubDate.second.fctr.nonNA## Warning: contrasts dropped from factor PubDate.minute.fctr.nonNA## Warning: contrasts dropped from factor PubDate.wkday.fctr.nonNA## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.6376812
## 3 0.2 0.6895706
## 4 0.3 0.7114094
## 5 0.4 0.7091172
## 6 0.5 0.7035330
## 7 0.6 0.6852459
## 8 0.7 0.6238859
## 9 0.8 0.5557730
## 10 0.9 0.4273128
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.All.X.glm.N
## 1 N 1577
## 2 Y 79
## Popular.fctr.predict.All.X.glm.Y
## 1 136
## 2 265
## Prediction
## Reference N Y
## N 1577 136
## Y 79 265
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.954789e-01 6.480478e-01 8.814466e-01 9.083722e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 4.319079e-16 1.339024e-04
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr.nonNA, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, H.npnct11.log, S.npnct16.log, S.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, S.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, S.npnct12.log, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, A.npnct13.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, S.npnct04.log, S.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 112.073 43.228
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9551762 0.3 0.7534077 0.8927341
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.902339 0.919243 0.5979193 0.927575
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7114094 0.8954789
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8814466 0.9083722 0.6480478 2104.317
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01343312 0.05134237
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_glm 2 0 406.602 526.51 119.908
## 3 fit.models_1_bayesglm 3 0 526.510 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr.nonNA, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, H.npnct11.log, S.npnct16.log, S.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, S.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, S.npnct12.log, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, A.npnct13.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, S.npnct04.log, S.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr"## Loading required package: arm
## Loading required package: MASS
## Loading required package: Matrix
## Loading required package: lme4
##
## arm (Version 1.8-5, built: 2015-05-13)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/Kaggle_NYTimes_blogs2_2## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8125 -0.2928 -0.1350 -0.0322 3.6961
##
## Coefficients:
## Estimate
## (Intercept) -11.058636
## A.ratio.sum.TfIdf.nwrds 0.716646
## WordCount.log.nonNA 1.096590
## S.ratio.sum.TfIdf.nwrds 0.532738
## H.ratio.sum.TfIdf.nwrds 0.343776
## myCategory.fctr.nonNA2 -0.380234
## myCategory.fctr.nonNA3 -2.163467
## myCategory.fctr.nonNA4 1.992529
## myCategory.fctr.nonNA5 -2.739170
## myCategory.fctr.nonNA6 0.255211
## myCategory.fctr.nonNA7 -0.699962
## myCategory.fctr.nonNA8 1.918404
## myCategory.fctr.nonNA9 0.672095
## myCategory.fctr.nonNA10 -0.346527
## myCategory.fctr.nonNA11 -1.145212
## myCategory.fctr.nonNA12 -0.496543
## myCategory.fctr.nonNA13 0.558493
## myCategory.fctr.nonNA14 -1.151776
## myCategory.fctr.nonNA15 3.393491
## myCategory.fctr.nonNA16 2.624510
## myCategory.fctr.nonNA17 -1.702252
## myCategory.fctr.nonNA18 1.930329
## myCategory.fctr.nonNA19 -0.355390
## myCategory.fctr.nonNA20 -0.666016
## PubDate.day.minutes.poly.1 8.154712
## H.sum.TfIdf 0.009501
## S.sum.TfIdf -0.027648
## A.sum.TfIdf -0.181520
## PubDate.hour.fctr.nonNA2 0.697763
## PubDate.hour.fctr.nonNA3 0.264994
## H.npnct19.log 1.435621
## A.ratio.nstopwrds.nwrds -2.150830
## S.ratio.nstopwrds.nwrds -3.041123
## PubDate.wkend -0.133010
## PubDate.day.minutes.poly.4 -6.553268
## PubDate.day.minutes.poly.2 31.616199
## S.npnct19.log 1.288567
## H.npnct08.log 0.973554
## PubDate.last10.log 0.125598
## PubDate.last1.log -0.048931
## H.ratio.nstopwrds.nwrds 3.658451
## H.npnct06.log 1.642058
## S.npnct01.log 1.283662
## H.npnct16.log -0.268419
## PubDate.day.minutes.poly.3 1.749578
## H.npnct01.log -1.176120
## A.npnct24.log 0.022809
## S.npnct24.log 0.022809
## H.npnct11.log 0.275590
## S.npnct16.log -0.360336
## S.npnct08.log 1.078045
## PubDate.last100.log 0.018002
## .rnorm -0.051562
## H.npnct05.log -0.940719
## PubDate.date.fctr.nonNA2 0.016221
## PubDate.date.fctr.nonNA3 -0.198710
## PubDate.date.fctr.nonNA4 -0.091650
## PubDate.date.fctr.nonNA5 0.194352
## PubDate.second.fctr.nonNA2 -0.004843
## PubDate.second.fctr.nonNA3 -0.048449
## PubDate.second.fctr.nonNA4 -0.346772
## H.npnct07.log 0.162866
## S.npnct03.log -4.837416
## A.npnct18.log -8.745066
## H.npnct12.log 0.357472
## A.npnct02.log -0.486498
## A.npnct17.log -0.059220
## S.npnct20.log -1.731276
## H.npnct02.log -2.203368
## S.npnct06.log -0.882600
## A.npnct14.log 0.469029
## H.npnct13.log -0.191240
## PubDate.minute.fctr.nonNA2 -0.071960
## PubDate.minute.fctr.nonNA3 -0.090722
## PubDate.minute.fctr.nonNA4 0.084965
## S.npnct12.log -0.162733
## PubDate.wkday.fctr.nonNA2 -0.319712
## PubDate.wkday.fctr.nonNA3 -0.919513
## PubDate.wkday.fctr.nonNA4 -0.478936
## PubDate.wkday.fctr.nonNA5 -0.804609
## PubDate.wkday.fctr.nonNA6 -0.602135
## PubDate.wkday.fctr.nonNA7 -0.810600
## S.npnct28.log -1.853321
## A.npnct28.log -1.837353
## A.npnct13.log 1.267837
## H.npnct04.log -1.860261
## S.npnct13.log -0.644422
## PubDate.day.minutes.poly.5 -5.276052
## H.npnct14.log -1.561253
## S.npnct04.log -1.335814
## S.npnct15.log -0.018958
## H.npnct15.log -0.886210
## H.nstopwrds.log -0.717224
## H.npnct28.log -0.434196
## S.npnct11.log -0.253668
## S.nstopwrds.log 1.030147
## A.nstopwrds.log 0.442395
## H.ndgts.log 0.535674
## S.ndgts.log -0.263671
## H.nuppr.log 1.737747
## H.nwrds.log 0.640223
## H.nchrs.log -0.339448
## S.nwrds.log 0.638609
## A.nwrds.log -0.098415
## H.nwrds.unq.log -0.898196
## S.nchrs.log 0.278130
## A.nwrds.unq.log -1.336223
## S.nwrds.unq.log -0.546324
## S.nuppr.log -0.487609
## `myCategory.fctr##:.clusterid.fctr1` 0.805677
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` -0.380234
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` -2.163467
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` 1.992529
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` -2.739170
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` 0.077148
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` -0.699962
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` 1.918404
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` 0.582160
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` 0.551215
## `myCategory.fctrForeign#World#:.clusterid.fctr1` -1.145212
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` -0.496543
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` 0.558493
## `myCategory.fctrmyOther:.clusterid.fctr1` -1.151776
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 0.668038
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 0.605451
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` -1.702252
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` 0.570457
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` -0.355390
## `myCategory.fctrTStyle##:.clusterid.fctr1` -0.666016
## `myCategory.fctr##:.clusterid.fctr2` -0.727435
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.241621
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` -0.120283
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -0.382960
## `myCategory.fctrForeign#World#:.clusterid.fctr2` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr2` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -0.002183
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.362064
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.340183
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr2` 0.000000
## `myCategory.fctr##:.clusterid.fctr3` 0.667874
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -0.440758
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.447448
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -1.401400
## `myCategory.fctrForeign#World#:.clusterid.fctr3` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr3` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.248682
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.298959
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 0.153012
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr3` 0.000000
## `myCategory.fctr##:.clusterid.fctr4` -0.726257
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` -0.104443
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 0.464907
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.342744
## `myCategory.fctrForeign#World#:.clusterid.fctr4` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr4` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 0.322673
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 0.110005
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 0.238532
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr4` 0.000000
## `myCategory.fctr##:.clusterid.fctr5` 0.303709
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 0.576077
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -0.366681
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 0.745016
## `myCategory.fctrForeign#World#:.clusterid.fctr5` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr5` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` -0.304427
## `myCategory.fctrScience#Health#:.clusterid.fctr5` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr5` 0.000000
## `myCategory.fctr##:.clusterid.fctr6` 0.472771
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -0.433725
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -1.333201
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -1.138399
## `myCategory.fctrForeign#World#:.clusterid.fctr6` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr6` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 0.443031
## `myCategory.fctrScience#Health#:.clusterid.fctr6` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr6` 0.000000
## `myCategory.fctr##:.clusterid.fctr7` -0.152832
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.180508
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 0.560338
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 0.957016
## `myCategory.fctrForeign#World#:.clusterid.fctr7` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr7` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 0.550974
## `myCategory.fctrScience#Health#:.clusterid.fctr7` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr7` 0.000000
## `myCategory.fctr##:.clusterid.fctr8` 1.528975
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` -0.089238
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 0.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -0.876487
## `myCategory.fctrForeign#World#:.clusterid.fctr8` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr8` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` -0.611705
## `myCategory.fctrScience#Health#:.clusterid.fctr8` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr8` 0.000000
## `myCategory.fctr##:.clusterid.fctr9` -1.349485
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.555111
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` 0.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -0.959606
## `myCategory.fctrForeign#World#:.clusterid.fctr9` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr9` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` 0.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr9` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr9` 0.000000
## `myCategory.fctr##:.clusterid.fctr10` -1.090821
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.768917
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` 0.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -1.036309
## `myCategory.fctrForeign#World#:.clusterid.fctr10` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr10` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` 0.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr10` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr10` 0.000000
## `myCategory.fctr##:.clusterid.fctr11` 0.347788
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -2.086598
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` 0.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 0.998376
## `myCategory.fctrForeign#World#:.clusterid.fctr11` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr11` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` 0.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr11` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr11` 0.000000
## `myCategory.fctr##:.clusterid.fctr12` 0.152141
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` -0.014968
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` 0.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 0.717189
## `myCategory.fctrForeign#World#:.clusterid.fctr12` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr12` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` 0.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr12` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr12` 0.000000
## `myCategory.fctr##:.clusterid.fctr13` -0.305965
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -0.287009
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` 0.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` -0.705015
## `myCategory.fctrForeign#World#:.clusterid.fctr13` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr13` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` 0.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr13` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr13` 0.000000
## `myCategory.fctr##:.clusterid.fctr14` -0.547517
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` -0.063946
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` 0.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` -0.740859
## `myCategory.fctrForeign#World#:.clusterid.fctr14` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr14` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` 0.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr14` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr14` 0.000000
## `myCategory.fctr##:.clusterid.fctr15` 0.376618
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.299783
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` 0.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` -0.772249
## `myCategory.fctrForeign#World#:.clusterid.fctr15` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr15` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` 0.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr15` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr15` 0.000000
## `myCategory.fctr##:.clusterid.fctr16` 0.030954
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 0.210405
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` 0.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 1.106188
## `myCategory.fctrForeign#World#:.clusterid.fctr16` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr16` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` 0.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr16` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr16` 0.000000
## `myCategory.fctr##:.clusterid.fctr17` -0.745181
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` 0.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` 0.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` 0.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` 0.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` 0.000000
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` 0.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` 0.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` 0.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` 0.000000
## `myCategory.fctrForeign#World#:.clusterid.fctr17` 0.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` 0.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` 0.000000
## `myCategory.fctrmyOther:.clusterid.fctr17` 0.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` 0.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr17` 0.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` 0.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` 0.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` 0.000000
## `myCategory.fctrTStyle##:.clusterid.fctr17` 0.000000
## Std. Error
## (Intercept) 5.400135
## A.ratio.sum.TfIdf.nwrds 2.084252
## WordCount.log.nonNA 0.093852
## S.ratio.sum.TfIdf.nwrds 2.081977
## H.ratio.sum.TfIdf.nwrds 0.211814
## myCategory.fctr.nonNA2 1.536873
## myCategory.fctr.nonNA3 1.996160
## myCategory.fctr.nonNA4 1.941104
## myCategory.fctr.nonNA5 2.380651
## myCategory.fctr.nonNA6 0.644005
## myCategory.fctr.nonNA7 1.554687
## myCategory.fctr.nonNA8 1.853728
## myCategory.fctr.nonNA9 0.808032
## myCategory.fctr.nonNA10 0.724763
## myCategory.fctr.nonNA11 1.926606
## myCategory.fctr.nonNA12 1.527946
## myCategory.fctr.nonNA13 1.513246
## myCategory.fctr.nonNA14 1.930284
## myCategory.fctr.nonNA15 0.784738
## myCategory.fctr.nonNA16 0.995777
## myCategory.fctr.nonNA17 2.025697
## myCategory.fctr.nonNA18 0.981056
## myCategory.fctr.nonNA19 1.569457
## myCategory.fctr.nonNA20 1.532484
## PubDate.day.minutes.poly.1 15.165977
## H.sum.TfIdf 0.076517
## S.sum.TfIdf 0.648874
## A.sum.TfIdf 0.649331
## PubDate.hour.fctr.nonNA2 0.452759
## PubDate.hour.fctr.nonNA3 0.515053
## H.npnct19.log 0.326701
## A.ratio.nstopwrds.nwrds 8.697421
## S.ratio.nstopwrds.nwrds 8.701449
## PubDate.wkend 0.405936
## PubDate.day.minutes.poly.4 9.835968
## PubDate.day.minutes.poly.2 12.052551
## S.npnct19.log 0.337435
## H.npnct08.log 0.470956
## PubDate.last10.log 0.132055
## PubDate.last1.log 0.045100
## H.ratio.nstopwrds.nwrds 2.425991
## H.npnct06.log 0.952210
## S.npnct01.log 1.880961
## H.npnct16.log 1.035135
## PubDate.day.minutes.poly.3 6.703331
## H.npnct01.log 1.298492
## A.npnct24.log 3.551711
## S.npnct24.log 3.551711
## H.npnct11.log 0.212683
## S.npnct16.log 1.310304
## S.npnct08.log 0.603621
## PubDate.last100.log 0.045327
## .rnorm 0.063346
## H.npnct05.log 2.491963
## PubDate.date.fctr.nonNA2 0.196346
## PubDate.date.fctr.nonNA3 0.193915
## PubDate.date.fctr.nonNA4 0.191420
## PubDate.date.fctr.nonNA5 0.204822
## PubDate.second.fctr.nonNA2 0.174580
## PubDate.second.fctr.nonNA3 0.171557
## PubDate.second.fctr.nonNA4 0.178310
## H.npnct07.log 0.209042
## S.npnct03.log 2.693910
## A.npnct18.log 15.562726
## H.npnct12.log 0.325335
## A.npnct02.log 1.726277
## A.npnct17.log 1.483618
## S.npnct20.log 2.233425
## H.npnct02.log 1.555425
## S.npnct06.log 1.456940
## A.npnct14.log 1.555679
## H.npnct13.log 0.200805
## PubDate.minute.fctr.nonNA2 0.184807
## PubDate.minute.fctr.nonNA3 0.178933
## PubDate.minute.fctr.nonNA4 0.184962
## S.npnct12.log 0.208430
## PubDate.wkday.fctr.nonNA2 0.455453
## PubDate.wkday.fctr.nonNA3 0.496309
## PubDate.wkday.fctr.nonNA4 0.487393
## PubDate.wkday.fctr.nonNA5 0.484081
## PubDate.wkday.fctr.nonNA6 0.486322
## PubDate.wkday.fctr.nonNA7 0.477145
## S.npnct28.log 6.613826
## A.npnct28.log 6.535669
## A.npnct13.log 1.187885
## H.npnct04.log 0.988550
## S.npnct13.log 1.166768
## PubDate.day.minutes.poly.5 6.700264
## H.npnct14.log 2.281577
## S.npnct04.log 0.668170
## S.npnct15.log 0.488498
## H.npnct15.log 0.308072
## H.nstopwrds.log 0.547277
## H.npnct28.log 1.460983
## S.npnct11.log 0.147878
## S.nstopwrds.log 1.590107
## A.nstopwrds.log 1.561104
## H.ndgts.log 0.247307
## S.ndgts.log 0.154777
## H.nuppr.log 0.654060
## H.nwrds.log 0.940945
## H.nchrs.log 0.520828
## S.nwrds.log 1.785505
## A.nwrds.log 1.766241
## H.nwrds.unq.log 0.448723
## S.nchrs.log 0.709871
## A.nwrds.unq.log 1.829742
## S.nwrds.unq.log 1.828658
## S.nuppr.log 0.152372
## `myCategory.fctr##:.clusterid.fctr1` 0.481625
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` 1.536873
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` 1.996160
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` 1.941104
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` 2.380651
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` 0.570396
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` 1.554687
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` 1.853728
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` 0.792714
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` 0.772788
## `myCategory.fctrForeign#World#:.clusterid.fctr1` 1.926606
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` 1.527946
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` 1.513246
## `myCategory.fctrmyOther:.clusterid.fctr1` 1.930284
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 0.708958
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 0.956473
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` 2.025697
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` 0.947477
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` 1.569457
## `myCategory.fctrTStyle##:.clusterid.fctr1` 1.532484
## `myCategory.fctr##:.clusterid.fctr2` 0.702169
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` 2.500000
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## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` 2.500000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` 2.500000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` 2.500000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 1.062338
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` 2.500000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` 2.500000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` 2.500000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 1.212002
## `myCategory.fctrForeign#World#:.clusterid.fctr16` 2.500000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` 2.500000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` 2.500000
## `myCategory.fctrmyOther:.clusterid.fctr16` 2.500000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` 2.500000
## `myCategory.fctrScience#Health#:.clusterid.fctr16` 2.500000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` 2.500000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` 2.500000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` 2.500000
## `myCategory.fctrTStyle##:.clusterid.fctr16` 2.500000
## `myCategory.fctr##:.clusterid.fctr17` 1.057059
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` 2.500000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` 2.500000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` 2.500000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` 2.500000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` 2.500000
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` 2.500000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` 2.500000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` 2.500000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` 2.500000
## `myCategory.fctrForeign#World#:.clusterid.fctr17` 2.500000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` 2.500000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` 2.500000
## `myCategory.fctrmyOther:.clusterid.fctr17` 2.500000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` 2.500000
## `myCategory.fctrScience#Health#:.clusterid.fctr17` 2.500000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` 2.500000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` 2.500000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` 2.500000
## `myCategory.fctrTStyle##:.clusterid.fctr17` 2.500000
## z value
## (Intercept) -2.048
## A.ratio.sum.TfIdf.nwrds 0.344
## WordCount.log.nonNA 11.684
## S.ratio.sum.TfIdf.nwrds 0.256
## H.ratio.sum.TfIdf.nwrds 1.623
## myCategory.fctr.nonNA2 -0.247
## myCategory.fctr.nonNA3 -1.084
## myCategory.fctr.nonNA4 1.026
## myCategory.fctr.nonNA5 -1.151
## myCategory.fctr.nonNA6 0.396
## myCategory.fctr.nonNA7 -0.450
## myCategory.fctr.nonNA8 1.035
## myCategory.fctr.nonNA9 0.832
## myCategory.fctr.nonNA10 -0.478
## myCategory.fctr.nonNA11 -0.594
## myCategory.fctr.nonNA12 -0.325
## myCategory.fctr.nonNA13 0.369
## myCategory.fctr.nonNA14 -0.597
## myCategory.fctr.nonNA15 4.324
## myCategory.fctr.nonNA16 2.636
## myCategory.fctr.nonNA17 -0.840
## myCategory.fctr.nonNA18 1.968
## myCategory.fctr.nonNA19 -0.226
## myCategory.fctr.nonNA20 -0.435
## PubDate.day.minutes.poly.1 0.538
## H.sum.TfIdf 0.124
## S.sum.TfIdf -0.043
## A.sum.TfIdf -0.280
## PubDate.hour.fctr.nonNA2 1.541
## PubDate.hour.fctr.nonNA3 0.514
## H.npnct19.log 4.394
## A.ratio.nstopwrds.nwrds -0.247
## S.ratio.nstopwrds.nwrds -0.349
## PubDate.wkend -0.328
## PubDate.day.minutes.poly.4 -0.666
## PubDate.day.minutes.poly.2 2.623
## S.npnct19.log 3.819
## H.npnct08.log 2.067
## PubDate.last10.log 0.951
## PubDate.last1.log -1.085
## H.ratio.nstopwrds.nwrds 1.508
## H.npnct06.log 1.724
## S.npnct01.log 0.682
## H.npnct16.log -0.259
## PubDate.day.minutes.poly.3 0.261
## H.npnct01.log -0.906
## A.npnct24.log 0.006
## S.npnct24.log 0.006
## H.npnct11.log 1.296
## S.npnct16.log -0.275
## S.npnct08.log 1.786
## PubDate.last100.log 0.397
## .rnorm -0.814
## H.npnct05.log -0.378
## PubDate.date.fctr.nonNA2 0.083
## PubDate.date.fctr.nonNA3 -1.025
## PubDate.date.fctr.nonNA4 -0.479
## PubDate.date.fctr.nonNA5 0.949
## PubDate.second.fctr.nonNA2 -0.028
## PubDate.second.fctr.nonNA3 -0.282
## PubDate.second.fctr.nonNA4 -1.945
## H.npnct07.log 0.779
## S.npnct03.log -1.796
## A.npnct18.log -0.562
## H.npnct12.log 1.099
## A.npnct02.log -0.282
## A.npnct17.log -0.040
## S.npnct20.log -0.775
## H.npnct02.log -1.417
## S.npnct06.log -0.606
## A.npnct14.log 0.301
## H.npnct13.log -0.952
## PubDate.minute.fctr.nonNA2 -0.389
## PubDate.minute.fctr.nonNA3 -0.507
## PubDate.minute.fctr.nonNA4 0.459
## S.npnct12.log -0.781
## PubDate.wkday.fctr.nonNA2 -0.702
## PubDate.wkday.fctr.nonNA3 -1.853
## PubDate.wkday.fctr.nonNA4 -0.983
## PubDate.wkday.fctr.nonNA5 -1.662
## PubDate.wkday.fctr.nonNA6 -1.238
## PubDate.wkday.fctr.nonNA7 -1.699
## S.npnct28.log -0.280
## A.npnct28.log -0.281
## A.npnct13.log 1.067
## H.npnct04.log -1.882
## S.npnct13.log -0.552
## PubDate.day.minutes.poly.5 -0.787
## H.npnct14.log -0.684
## S.npnct04.log -1.999
## S.npnct15.log -0.039
## H.npnct15.log -2.877
## H.nstopwrds.log -1.311
## H.npnct28.log -0.297
## S.npnct11.log -1.715
## S.nstopwrds.log 0.648
## A.nstopwrds.log 0.283
## H.ndgts.log 2.166
## S.ndgts.log -1.704
## H.nuppr.log 2.657
## H.nwrds.log 0.680
## H.nchrs.log -0.652
## S.nwrds.log 0.358
## A.nwrds.log -0.056
## H.nwrds.unq.log -2.002
## S.nchrs.log 0.392
## A.nwrds.unq.log -0.730
## S.nwrds.unq.log -0.299
## S.nuppr.log -3.200
## `myCategory.fctr##:.clusterid.fctr1` 1.673
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` -0.247
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` -1.084
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` 1.026
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` -1.151
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` 0.135
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` -0.450
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` 1.035
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` 0.734
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` 0.713
## `myCategory.fctrForeign#World#:.clusterid.fctr1` -0.594
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` -0.325
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` 0.369
## `myCategory.fctrmyOther:.clusterid.fctr1` -0.597
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 0.942
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 0.633
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` -0.840
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` 0.602
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` -0.226
## `myCategory.fctrTStyle##:.clusterid.fctr1` -0.435
## `myCategory.fctr##:.clusterid.fctr2` -1.036
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.371
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` -0.138
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -0.368
## `myCategory.fctrForeign#World#:.clusterid.fctr2` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr2` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -0.003
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.367
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.355
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr2` 0.000
## `myCategory.fctr##:.clusterid.fctr3` 1.218
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -0.621
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.553
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -0.911
## `myCategory.fctrForeign#World#:.clusterid.fctr3` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr3` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.328
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.304
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 0.157
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr3` 0.000
## `myCategory.fctr##:.clusterid.fctr4` -0.768
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` -0.127
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 0.573
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.741
## `myCategory.fctrForeign#World#:.clusterid.fctr4` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr4` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 0.401
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 0.112
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 0.228
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr4` 0.000
## `myCategory.fctr##:.clusterid.fctr5` 0.417
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 0.826
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -0.385
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 0.679
## `myCategory.fctrForeign#World#:.clusterid.fctr5` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr5` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` -0.384
## `myCategory.fctrScience#Health#:.clusterid.fctr5` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr5` 0.000
## `myCategory.fctr##:.clusterid.fctr6` 0.786
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -0.513
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -0.831
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -0.724
## `myCategory.fctrForeign#World#:.clusterid.fctr6` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr6` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 0.514
## `myCategory.fctrScience#Health#:.clusterid.fctr6` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr6` 0.000
## `myCategory.fctr##:.clusterid.fctr7` -0.227
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.233
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 0.612
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 0.849
## `myCategory.fctrForeign#World#:.clusterid.fctr7` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr7` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 0.590
## `myCategory.fctrScience#Health#:.clusterid.fctr7` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr7` 0.000
## `myCategory.fctr##:.clusterid.fctr8` 2.623
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` -0.116
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -0.533
## `myCategory.fctrForeign#World#:.clusterid.fctr8` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr8` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` -0.677
## `myCategory.fctrScience#Health#:.clusterid.fctr8` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr8` 0.000
## `myCategory.fctr##:.clusterid.fctr9` -0.882
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.486
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -0.592
## `myCategory.fctrForeign#World#:.clusterid.fctr9` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr9` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` 0.000
## `myCategory.fctrScience#Health#:.clusterid.fctr9` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr9` 0.000
## `myCategory.fctr##:.clusterid.fctr10` -1.175
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 1.078
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -0.647
## `myCategory.fctrForeign#World#:.clusterid.fctr10` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr10` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` 0.000
## `myCategory.fctrScience#Health#:.clusterid.fctr10` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr10` 0.000
## `myCategory.fctr##:.clusterid.fctr11` 0.495
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -1.392
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 0.879
## `myCategory.fctrForeign#World#:.clusterid.fctr11` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr11` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` 0.000
## `myCategory.fctrScience#Health#:.clusterid.fctr11` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr11` 0.000
## `myCategory.fctr##:.clusterid.fctr12` 0.152
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` -0.018
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 0.638
## `myCategory.fctrForeign#World#:.clusterid.fctr12` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr12` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` 0.000
## `myCategory.fctrScience#Health#:.clusterid.fctr12` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr12` 0.000
## `myCategory.fctr##:.clusterid.fctr13` -0.345
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -0.341
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` -0.412
## `myCategory.fctrForeign#World#:.clusterid.fctr13` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr13` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` 0.000
## `myCategory.fctrScience#Health#:.clusterid.fctr13` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr13` 0.000
## `myCategory.fctr##:.clusterid.fctr14` -0.570
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` -0.027
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` -0.436
## `myCategory.fctrForeign#World#:.clusterid.fctr14` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr14` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` 0.000
## `myCategory.fctrScience#Health#:.clusterid.fctr14` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr14` 0.000
## `myCategory.fctr##:.clusterid.fctr15` 0.442
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.327
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` -0.456
## `myCategory.fctrForeign#World#:.clusterid.fctr15` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr15` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` 0.000
## `myCategory.fctrScience#Health#:.clusterid.fctr15` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr15` 0.000
## `myCategory.fctr##:.clusterid.fctr16` 0.030
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 0.198
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 0.913
## `myCategory.fctrForeign#World#:.clusterid.fctr16` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr16` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` 0.000
## `myCategory.fctrScience#Health#:.clusterid.fctr16` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr16` 0.000
## `myCategory.fctr##:.clusterid.fctr17` -0.705
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` 0.000
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` 0.000
## `myCategory.fctrForeign#World#:.clusterid.fctr17` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr17` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` 0.000
## `myCategory.fctrScience#Health#:.clusterid.fctr17` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr17` 0.000
## Pr(>|z|)
## (Intercept) 0.040575
## A.ratio.sum.TfIdf.nwrds 0.730968
## WordCount.log.nonNA < 2e-16
## S.ratio.sum.TfIdf.nwrds 0.798043
## H.ratio.sum.TfIdf.nwrds 0.104588
## myCategory.fctr.nonNA2 0.804593
## myCategory.fctr.nonNA3 0.278447
## myCategory.fctr.nonNA4 0.304659
## myCategory.fctr.nonNA5 0.249898
## myCategory.fctr.nonNA6 0.691893
## myCategory.fctr.nonNA7 0.652547
## myCategory.fctr.nonNA8 0.300720
## myCategory.fctr.nonNA9 0.405540
## myCategory.fctr.nonNA10 0.632562
## myCategory.fctr.nonNA11 0.552232
## myCategory.fctr.nonNA12 0.745201
## myCategory.fctr.nonNA13 0.712076
## myCategory.fctr.nonNA14 0.550716
## myCategory.fctr.nonNA15 1.53e-05
## myCategory.fctr.nonNA16 0.008398
## myCategory.fctr.nonNA17 0.400724
## myCategory.fctr.nonNA18 0.049114
## myCategory.fctr.nonNA19 0.820858
## myCategory.fctr.nonNA20 0.663853
## PubDate.day.minutes.poly.1 0.590786
## H.sum.TfIdf 0.901180
## S.sum.TfIdf 0.966014
## A.sum.TfIdf 0.779823
## PubDate.hour.fctr.nonNA2 0.123283
## PubDate.hour.fctr.nonNA3 0.606904
## H.npnct19.log 1.11e-05
## A.ratio.nstopwrds.nwrds 0.804680
## S.ratio.nstopwrds.nwrds 0.726717
## PubDate.wkend 0.743166
## PubDate.day.minutes.poly.4 0.505248
## PubDate.day.minutes.poly.2 0.008711
## S.npnct19.log 0.000134
## H.npnct08.log 0.038717
## PubDate.last10.log 0.341553
## PubDate.last1.log 0.277946
## H.ratio.nstopwrds.nwrds 0.131549
## H.npnct06.log 0.084623
## S.npnct01.log 0.494954
## H.npnct16.log 0.795397
## PubDate.day.minutes.poly.3 0.794092
## H.npnct01.log 0.365064
## A.npnct24.log 0.994876
## S.npnct24.log 0.994876
## H.npnct11.log 0.195051
## S.npnct16.log 0.783315
## S.npnct08.log 0.074105
## PubDate.last100.log 0.691252
## .rnorm 0.415658
## H.npnct05.log 0.705801
## PubDate.date.fctr.nonNA2 0.934160
## PubDate.date.fctr.nonNA3 0.305493
## PubDate.date.fctr.nonNA4 0.632087
## PubDate.date.fctr.nonNA5 0.342681
## PubDate.second.fctr.nonNA2 0.977867
## PubDate.second.fctr.nonNA3 0.777632
## PubDate.second.fctr.nonNA4 0.051802
## H.npnct07.log 0.435916
## S.npnct03.log 0.072545
## A.npnct18.log 0.574168
## H.npnct12.log 0.271864
## A.npnct02.log 0.778082
## A.npnct17.log 0.968160
## S.npnct20.log 0.438242
## H.npnct02.log 0.156609
## S.npnct06.log 0.544654
## A.npnct14.log 0.763037
## H.npnct13.log 0.340911
## PubDate.minute.fctr.nonNA2 0.696996
## PubDate.minute.fctr.nonNA3 0.612141
## PubDate.minute.fctr.nonNA4 0.645974
## S.npnct12.log 0.434945
## PubDate.wkday.fctr.nonNA2 0.482700
## PubDate.wkday.fctr.nonNA3 0.063925
## PubDate.wkday.fctr.nonNA4 0.325780
## PubDate.wkday.fctr.nonNA5 0.096485
## PubDate.wkday.fctr.nonNA6 0.215664
## PubDate.wkday.fctr.nonNA7 0.089346
## S.npnct28.log 0.779309
## A.npnct28.log 0.778613
## A.npnct13.log 0.285833
## H.npnct04.log 0.059862
## S.npnct13.log 0.580733
## PubDate.day.minutes.poly.5 0.431025
## H.npnct14.log 0.493794
## S.npnct04.log 0.045585
## S.npnct15.log 0.969044
## H.npnct15.log 0.004019
## H.nstopwrds.log 0.190016
## H.npnct28.log 0.766318
## S.npnct11.log 0.086275
## S.nstopwrds.log 0.517084
## A.nstopwrds.log 0.776881
## H.ndgts.log 0.030309
## S.ndgts.log 0.088464
## H.nuppr.log 0.007887
## H.nwrds.log 0.496249
## H.nchrs.log 0.514564
## S.nwrds.log 0.720596
## A.nwrds.log 0.955565
## H.nwrds.unq.log 0.045320
## S.nchrs.log 0.695204
## A.nwrds.unq.log 0.465219
## S.nwrds.unq.log 0.765126
## S.nuppr.log 0.001374
## `myCategory.fctr##:.clusterid.fctr1` 0.094361
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` 0.804593
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` 0.278447
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` 0.304659
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` 0.249898
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` 0.892411
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` 0.652547
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` 0.300720
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` 0.462712
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` 0.475672
## `myCategory.fctrForeign#World#:.clusterid.fctr1` 0.552232
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` 0.745201
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` 0.712076
## `myCategory.fctrmyOther:.clusterid.fctr1` 0.550716
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 0.346049
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 0.526731
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` 0.400724
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` 0.547121
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` 0.820858
## `myCategory.fctrTStyle##:.clusterid.fctr1` 0.663853
## `myCategory.fctr##:.clusterid.fctr2` 0.300210
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.710455
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.889992
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.712623
## `myCategory.fctrForeign#World#:.clusterid.fctr2` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr2` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.997694
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.713938
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.722505
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr2` 1.000000
## `myCategory.fctr##:.clusterid.fctr3` 0.223269
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 0.534853
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.580305
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 0.362233
## `myCategory.fctrForeign#World#:.clusterid.fctr3` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr3` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.743207
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.761034
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 0.874996
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr3` 1.000000
## `myCategory.fctr##:.clusterid.fctr4` 0.442372
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 0.899202
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 0.566923
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 0.081727
## `myCategory.fctrForeign#World#:.clusterid.fctr4` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr4` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 0.688565
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 0.910912
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 0.819547
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr4` 1.000000
## `myCategory.fctr##:.clusterid.fctr5` 0.676978
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 0.408858
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 0.700549
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 0.496951
## `myCategory.fctrForeign#World#:.clusterid.fctr5` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr5` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 0.700694
## `myCategory.fctrScience#Health#:.clusterid.fctr5` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr5` 1.000000
## `myCategory.fctr##:.clusterid.fctr6` 0.432126
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 0.607960
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 0.405824
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 0.469045
## `myCategory.fctrForeign#World#:.clusterid.fctr6` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr6` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 0.606928
## `myCategory.fctrScience#Health#:.clusterid.fctr6` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr6` 1.000000
## `myCategory.fctr##:.clusterid.fctr7` 0.820675
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.816042
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 0.540863
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 0.395865
## `myCategory.fctrForeign#World#:.clusterid.fctr7` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr7` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 0.555017
## `myCategory.fctrScience#Health#:.clusterid.fctr7` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr7` 1.000000
## `myCategory.fctr##:.clusterid.fctr8` 0.008724
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 0.907373
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 1.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 0.593849
## `myCategory.fctrForeign#World#:.clusterid.fctr8` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr8` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 0.498366
## `myCategory.fctrScience#Health#:.clusterid.fctr8` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr8` 1.000000
## `myCategory.fctr##:.clusterid.fctr9` 0.377730
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.626765
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` 1.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 0.553774
## `myCategory.fctrForeign#World#:.clusterid.fctr9` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr9` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` 1.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr9` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr9` 1.000000
## `myCategory.fctr##:.clusterid.fctr10` 0.239968
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.280977
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` 1.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 0.517920
## `myCategory.fctrForeign#World#:.clusterid.fctr10` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr10` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` 1.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr10` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr10` 1.000000
## `myCategory.fctr##:.clusterid.fctr11` 0.620414
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 0.163825
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` 1.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 0.379496
## `myCategory.fctrForeign#World#:.clusterid.fctr11` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr11` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` 1.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr11` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr11` 1.000000
## `myCategory.fctr##:.clusterid.fctr12` 0.879413
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 0.986034
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` 1.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 0.523377
## `myCategory.fctrForeign#World#:.clusterid.fctr12` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr12` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` 1.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr12` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr12` 1.000000
## `myCategory.fctr##:.clusterid.fctr13` 0.729959
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 0.733152
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` 1.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 0.680527
## `myCategory.fctrForeign#World#:.clusterid.fctr13` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr13` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` 1.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr13` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr13` 1.000000
## `myCategory.fctr##:.clusterid.fctr14` 0.568706
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.978851
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` 1.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 0.662650
## `myCategory.fctrForeign#World#:.clusterid.fctr14` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr14` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` 1.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr14` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr14` 1.000000
## `myCategory.fctr##:.clusterid.fctr15` 0.658732
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.743718
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` 1.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 0.648189
## `myCategory.fctrForeign#World#:.clusterid.fctr15` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr15` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` 1.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr15` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr15` 1.000000
## `myCategory.fctr##:.clusterid.fctr16` 0.976383
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 0.842999
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` 1.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 0.361403
## `myCategory.fctrForeign#World#:.clusterid.fctr16` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr16` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` 1.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr16` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr16` 1.000000
## `myCategory.fctr##:.clusterid.fctr17` 0.480837
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` 1.000000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` 1.000000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` 1.000000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` 1.000000
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` 1.000000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` 1.000000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` 1.000000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` 1.000000
## `myCategory.fctrForeign#World#:.clusterid.fctr17` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` 1.000000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` 1.000000
## `myCategory.fctrmyOther:.clusterid.fctr17` 1.000000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` 1.000000
## `myCategory.fctrScience#Health#:.clusterid.fctr17` 1.000000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` 1.000000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` 1.000000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` 1.000000
## `myCategory.fctrTStyle##:.clusterid.fctr17` 1.000000
##
## (Intercept) *
## A.ratio.sum.TfIdf.nwrds
## WordCount.log.nonNA ***
## S.ratio.sum.TfIdf.nwrds
## H.ratio.sum.TfIdf.nwrds
## myCategory.fctr.nonNA2
## myCategory.fctr.nonNA3
## myCategory.fctr.nonNA4
## myCategory.fctr.nonNA5
## myCategory.fctr.nonNA6
## myCategory.fctr.nonNA7
## myCategory.fctr.nonNA8
## myCategory.fctr.nonNA9
## myCategory.fctr.nonNA10
## myCategory.fctr.nonNA11
## myCategory.fctr.nonNA12
## myCategory.fctr.nonNA13
## myCategory.fctr.nonNA14
## myCategory.fctr.nonNA15 ***
## myCategory.fctr.nonNA16 **
## myCategory.fctr.nonNA17
## myCategory.fctr.nonNA18 *
## myCategory.fctr.nonNA19
## myCategory.fctr.nonNA20
## PubDate.day.minutes.poly.1
## H.sum.TfIdf
## S.sum.TfIdf
## A.sum.TfIdf
## PubDate.hour.fctr.nonNA2
## PubDate.hour.fctr.nonNA3
## H.npnct19.log ***
## A.ratio.nstopwrds.nwrds
## S.ratio.nstopwrds.nwrds
## PubDate.wkend
## PubDate.day.minutes.poly.4
## PubDate.day.minutes.poly.2 **
## S.npnct19.log ***
## H.npnct08.log *
## PubDate.last10.log
## PubDate.last1.log
## H.ratio.nstopwrds.nwrds
## H.npnct06.log .
## S.npnct01.log
## H.npnct16.log
## PubDate.day.minutes.poly.3
## H.npnct01.log
## A.npnct24.log
## S.npnct24.log
## H.npnct11.log
## S.npnct16.log
## S.npnct08.log .
## PubDate.last100.log
## .rnorm
## H.npnct05.log
## PubDate.date.fctr.nonNA2
## PubDate.date.fctr.nonNA3
## PubDate.date.fctr.nonNA4
## PubDate.date.fctr.nonNA5
## PubDate.second.fctr.nonNA2
## PubDate.second.fctr.nonNA3
## PubDate.second.fctr.nonNA4 .
## H.npnct07.log
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## S.npnct20.log
## H.npnct02.log
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## A.npnct14.log
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## PubDate.wkday.fctr.nonNA3 .
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## PubDate.wkday.fctr.nonNA5 .
## PubDate.wkday.fctr.nonNA6
## PubDate.wkday.fctr.nonNA7 .
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## A.npnct28.log
## A.npnct13.log
## H.npnct04.log .
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## H.npnct28.log
## S.npnct11.log .
## S.nstopwrds.log
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## H.ndgts.log *
## S.ndgts.log .
## H.nuppr.log **
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## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16`
## `myCategory.fctrmyOther:.clusterid.fctr16`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16`
## `myCategory.fctrScience#Health#:.clusterid.fctr16`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16`
## `myCategory.fctrTStyle##:.clusterid.fctr16`
## `myCategory.fctr##:.clusterid.fctr17`
## `myCategory.fctr#Multimedia#:.clusterid.fctr17`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17`
## `myCategory.fctrmyOther:.clusterid.fctr17`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17`
## `myCategory.fctrScience#Health#:.clusterid.fctr17`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17`
## `myCategory.fctrTStyle##:.clusterid.fctr17`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1771.5 on 4026 degrees of freedom
## AIC: 2669.5
##
## Number of Fisher Scoring iterations: 20
##
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor myCategory.fctr.nonNA## Warning: contrasts dropped from factor PubDate.hour.fctr.nonNA## Warning: contrasts dropped from factor PubDate.date.fctr.nonNA## Warning: contrasts dropped from factor PubDate.second.fctr.nonNA## Warning: contrasts dropped from factor PubDate.minute.fctr.nonNA## Warning: contrasts dropped from factor PubDate.wkday.fctr.nonNA
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.6682464
## 3 0.2 0.7323147
## 4 0.3 0.7509340
## 5 0.4 0.7451244
## 6 0.5 0.7390367
## 7 0.6 0.7204874
## 8 0.7 0.6589595
## 9 0.8 0.5758123
## 10 0.9 0.4037657
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.All.X.bayesglm.N
## 1 N 3472
## 2 Y 146
## Popular.fctr.predict.All.X.bayesglm.Y
## 1 254
## 2 603
## Prediction
## Reference N Y
## N 3472 254
## Y 146 603
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.106145e-01 6.967676e-01 9.018735e-01 9.188152e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 1.328196e-51 8.795423e-08
## [1] " calling mypredict_mdl for OOB:"## Warning: contrasts dropped from factor myCategory.fctr.nonNA## Warning: contrasts dropped from factor PubDate.hour.fctr.nonNA## Warning: contrasts dropped from factor PubDate.date.fctr.nonNA## Warning: contrasts dropped from factor PubDate.second.fctr.nonNA## Warning: contrasts dropped from factor PubDate.minute.fctr.nonNA## Warning: contrasts dropped from factor PubDate.wkday.fctr.nonNA
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.6371134
## 3 0.2 0.6929716
## 4 0.3 0.7150538
## 5 0.4 0.7153285
## 6 0.5 0.7078825
## 7 0.6 0.6766169
## 8 0.7 0.6236559
## 9 0.8 0.5522682
## 10 0.9 0.4000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.All.X.bayesglm.N
## 1 N 1617
## 2 Y 99
## Popular.fctr.predict.All.X.bayesglm.Y
## 1 96
## 2 245
## Prediction
## Reference N Y
## N 1617 96
## Y 99 245
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.052018e-01 6.584619e-01 8.917156e-01 9.175197e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 2.633690e-21 8.861141e-01
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr.nonNA, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, H.npnct11.log, S.npnct16.log, S.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, S.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, S.npnct12.log, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, A.npnct13.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, S.npnct04.log, S.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 56.701 27.659
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.954399 0.3 0.750934 0.9068153
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9018735 0.9188152 0.6466278 0.9331243
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7153285 0.9052018
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8917156 0.9175197 0.6584619 2669.46
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.001372296 0.001120182
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_bayesglm 3 0 526.510 591.028 64.518
## 4 fit.models_1_rpart 4 0 591.029 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr.nonNA, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, H.npnct11.log, S.npnct16.log, S.npnct08.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, S.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, S.npnct12.log, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, A.npnct13.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, S.npnct04.log, S.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.034 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4475
##
## CP nsplit rel error
## 1 0.26969292 0 1.0000000
## 2 0.08411215 1 0.7303071
## 3 0.03404539 2 0.6461949
##
## Variable importance
## myCategory.fctr.nonNA15
## 37
## myCategory.fctrOpEd#Opinion#:.clusterid.fctr1
## 20
## myCategory.fctr.nonNA8
## 13
## myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1
## 13
## myCategory.fctrOpEd#Opinion#:.clusterid.fctr3
## 4
## S.nchrs.log
## 4
## A.nwrds.unq.log
## 4
## S.nwrds.unq.log
## 4
## H.nchrs.log
## 1
##
## Node number 1: 4475 observations, complexity param=0.2696929
## predicted class=N expected loss=0.1673743 P(node) =1
## class counts: 3726 749
## probabilities: 0.833 0.167
## left son=2 (4103 obs) right son=3 (372 obs)
## Primary splits:
## myCategory.fctr.nonNA15 < 0.5 to the left, improve=296.16030, (0 missing)
## myCategory.fctrOpEd#Opinion#:.clusterid.fctr1 < 0.5 to the left, improve=195.15790, (0 missing)
## WordCount.log.nonNA < 6.524296 to the left, improve=106.82430, (0 missing)
## myCategory.fctr.nonNA8 < 0.5 to the left, improve= 85.77765, (0 missing)
## myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1 < 0.5 to the left, improve= 85.77765, (0 missing)
## Surrogate splits:
## myCategory.fctrOpEd#Opinion#:.clusterid.fctr1 < 0.5 to the left, agree=0.962, adj=0.543, (0 split)
## S.nchrs.log < 3.725621 to the right, agree=0.926, adj=0.108, (0 split)
## myCategory.fctrOpEd#Opinion#:.clusterid.fctr3 < 0.5 to the left, agree=0.926, adj=0.108, (0 split)
## A.nwrds.unq.log < 1.497866 to the right, agree=0.925, adj=0.102, (0 split)
## S.nwrds.unq.log < 1.497866 to the right, agree=0.925, adj=0.099, (0 split)
##
## Node number 2: 4103 observations, complexity param=0.08411215
## predicted class=N expected loss=0.1126005 P(node) =0.9168715
## class counts: 3641 462
## probabilities: 0.887 0.113
## left son=4 (4020 obs) right son=5 (83 obs)
## Primary splits:
## myCategory.fctr.nonNA8 < 0.5 to the left, improve=99.65082, (0 missing)
## myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1 < 0.5 to the left, improve=99.65082, (0 missing)
## WordCount.log.nonNA < 6.485398 to the left, improve=95.05111, (0 missing)
## myCategory.fctr.nonNA16 < 0.5 to the left, improve=65.81543, (0 missing)
## myCategory.fctr.nonNA18 < 0.5 to the left, improve=50.98430, (0 missing)
## Surrogate splits:
## myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1 < 0.5 to the left, agree=1.000, adj=1.000, (0 split)
## H.nchrs.log < 2.35024 to the right, agree=0.981, adj=0.060, (0 split)
## A.ratio.sum.TfIdf.nwrds < 1.496104 to the left, agree=0.980, adj=0.024, (0 split)
## S.ratio.sum.TfIdf.nwrds < 1.496104 to the left, agree=0.980, adj=0.024, (0 split)
## H.ratio.sum.TfIdf.nwrds < 6.393259 to the left, agree=0.980, adj=0.024, (0 split)
##
## Node number 3: 372 observations
## predicted class=Y expected loss=0.2284946 P(node) =0.08312849
## class counts: 85 287
## probabilities: 0.228 0.772
##
## Node number 4: 4020 observations
## predicted class=N expected loss=0.09676617 P(node) =0.898324
## class counts: 3631 389
## probabilities: 0.903 0.097
##
## Node number 5: 83 observations
## predicted class=Y expected loss=0.1204819 P(node) =0.01854749
## class counts: 10 73
## probabilities: 0.120 0.880
##
## n= 4475
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4475 749 N (0.83262570 0.16737430)
## 2) myCategory.fctr.nonNA15< 0.5 4103 462 N (0.88739946 0.11260054)
## 4) myCategory.fctr.nonNA8< 0.5 4020 389 N (0.90323383 0.09676617) *
## 5) myCategory.fctr.nonNA8>=0.5 83 10 Y (0.12048193 0.87951807) *
## 3) myCategory.fctr.nonNA15>=0.5 372 85 Y (0.22849462 0.77150538) *
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor myCategory.fctr.nonNA## Warning: contrasts dropped from factor PubDate.hour.fctr.nonNA## Warning: contrasts dropped from factor PubDate.date.fctr.nonNA## Warning: contrasts dropped from factor PubDate.second.fctr.nonNA## Warning: contrasts dropped from factor PubDate.minute.fctr.nonNA## Warning: contrasts dropped from factor PubDate.wkday.fctr.nonNA
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.5980066
## 3 0.2 0.5980066
## 4 0.3 0.5980066
## 5 0.4 0.5980066
## 6 0.5 0.5980066
## 7 0.6 0.5980066
## 8 0.7 0.5980066
## 9 0.8 0.1754808
## 10 0.9 0.0000000
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 3631
## 2 Y 389
## Popular.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 95
## 2 360
## Prediction
## Reference N Y
## N 3631 95
## Y 389 360
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.918436e-01 5.397881e-01 8.823752e-01 9.007970e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 2.395722e-29 1.814733e-40
## [1] " calling mypredict_mdl for OOB:"## Warning: contrasts dropped from factor myCategory.fctr.nonNA## Warning: contrasts dropped from factor PubDate.hour.fctr.nonNA## Warning: contrasts dropped from factor PubDate.date.fctr.nonNA## Warning: contrasts dropped from factor PubDate.second.fctr.nonNA## Warning: contrasts dropped from factor PubDate.minute.fctr.nonNA## Warning: contrasts dropped from factor PubDate.wkday.fctr.nonNA
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.5650558
## 3 0.2 0.5650558
## 4 0.3 0.5650558
## 5 0.4 0.5650558
## 6 0.5 0.5650558
## 7 0.6 0.5650558
## 8 0.7 0.5650558
## 9 0.8 0.1562500
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 1671
## 2 Y 192
## Popular.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 42
## 2 152
## Prediction
## Reference N Y
## N 1671 42
## Y 192 152
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.862421e-01 5.054039e-01 8.717239e-01 8.996488e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 5.783557e-12 2.026854e-22
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr.nonNA, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, H.npnct11.log, S.npnct16.log, S.npnct08.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, S.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, S.npnct12.log, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, A.npnct13.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, S.npnct04.log, S.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 21.544 4.791
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7281697 0.7 0.5980066 0.8989947
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8823752 0.900797 0.5796742 0.7084504
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.5650558 0.8862421
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8717239 0.8996488 0.5054039
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008429336 0.05007835# User specified
# easier to exclude features
#model_id_pfx <- "";
# indep_vars_vctr <- setdiff(names(glb_fitobs_df),
# union(union(glb_rsp_var, glb_exclude_vars_as_features),
# c("<feat1_name>", "<feat2_name>")))
# method <- ""
# easier to include features
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
#table(glb_allobs_df$myCategory, glb_allobs_df$H.P.readers.respond, glb_allobs_df[, glb_rsp_var], useNA="ifany")
model_id <- "Rank9"; indep_vars_vctr <- c(
"myCategory.fctr*WordCount.log.nonNA"
,"myCategory.fctr:.clusterid.fctr"
,"myCategory.fctr*H.npnct19.log"
,"myCategory.fctr:PubDate.day.minutes.poly.1"
,"myCategory.fctr:PubDate.day.minutes.poly.2"
,"myCategory.fctr:PubDate.day.minutes.poly.3"
,"myCategory.fctr:PubDate.day.minutes.poly.4"
,"myCategory.fctr:PubDate.day.minutes.poly.5"
,"myCategory.fctr:PubDate.wkend"
,"myCategory.fctr:H.npnct28.log"
); method <- "glm"
for (method in c("glm", "bayesglm")) {
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(model_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
}## [1] "fitting model: Rank9.glm"
## [1] " indep_vars: myCategory.fctr*WordCount.log.nonNA, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5, myCategory.fctr:PubDate.wkend, myCategory.fctr:H.npnct28.log"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 121, 2403
## Warning: not plotting observations with leverage one:
## 121, 2403
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4280 -0.2438 -0.0387 0.0000 8.4904
##
## Coefficients: (275 not defined because of singularities)
## Estimate
## (Intercept) -1.363e+01
## `myCategory.fctr#Multimedia#` -9.548e+00
## `myCategory.fctr#Opinion#Room For Debate` 8.385e+02
## `myCategory.fctr#Opinion#The Public Editor` 3.115e+01
## `myCategory.fctr#U.S.#Education` -7.941e+00
## `myCategory.fctrBusiness#Business Day#Dealbook` -9.367e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 6.282e+15
## `myCategory.fctrBusiness#Crosswords/Games#` 2.371e+03
## `myCategory.fctrBusiness#Technology#` -1.944e+00
## `myCategory.fctrCulture#Arts#` 5.996e+00
## `myCategory.fctrForeign#World#` -7.941e+00
## `myCategory.fctrForeign#World#Asia Pacific` -2.712e+01
## `myCategory.fctrMetro#N.Y. / Region#` 9.122e+00
## myCategory.fctrmyOther -7.941e+00
## `myCategory.fctrOpEd#Opinion#` 1.406e+01
## `myCategory.fctrScience#Health#` 1.773e+00
## `myCategory.fctrStyles##Fashion` -7.941e+00
## `myCategory.fctrStyles#U.S.#` 8.893e+00
## `myCategory.fctrTravel#Travel#` 1.231e+02
## `myCategory.fctrTStyle##` -4.388e+01
## WordCount.log.nonNA 1.829e+00
## H.npnct19.log 2.059e+00
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` -1.074e+00
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` -8.504e+01
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` -2.502e+00
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` -1.829e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 1.181e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` -4.894e+14
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 1.875e+00
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 3.293e-01
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` -1.215e+00
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` -1.829e+00
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` -1.775e+00
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` -1.406e+00
## `myCategory.fctrmyOther:WordCount.log.nonNA` -1.829e+00
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` -1.669e+00
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 2.941e-01
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` -1.829e+00
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` -1.038e+00
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 8.907e+01
## `myCategory.fctrTStyle##:WordCount.log.nonNA` -1.614e-01
## `myCategory.fctr##:.clusterid.fctr2` -2.426e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 3.818e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` -3.871e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -3.392e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -9.280e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -3.170e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -5.621e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -1.432e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -1.600e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 3.262e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -1.767e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` -1.020e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -2.810e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -2.782e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` -1.076e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 1.774e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 6.479e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.440e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` -5.855e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` -6.425e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 3.110e-02
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` -6.432e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 1.021e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -2.175e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 7.350e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` -1.122e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` -3.381e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 5.490e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -1.852e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -1.732e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` -5.498e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` -1.107e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 7.047e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 2.109e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 1.191e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 2.782e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 6.307e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 2.376e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -1.794e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` -1.168e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -2.008e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 2.077e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -1.746e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` -2.354e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 8.351e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -1.855e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` -1.956e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -1.860e+01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 2.144e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` -9.373e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` -1.194e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.351e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` -1.402e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -1.841e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` -1.756e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` -1.616e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` -1.986e+01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` -1.755e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` -5.067e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 2.334e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` -1.777e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` -5.802e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` -3.156e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 1.501e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` -1.595e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` -8.291e+00
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 6.405e+01
## `myCategory.fctr#U.S.#Education:H.npnct19.log` -2.059e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` -2.111e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 8.956e+13
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 1.306e+00
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 1.217e-01
## `myCategory.fctrForeign#World#:H.npnct19.log` -2.059e+00
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` -8.240e+00
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 6.374e-01
## `myCategory.fctrmyOther:H.npnct19.log` -2.059e+00
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` -1.368e+00
## `myCategory.fctrScience#Health#:H.npnct19.log` -1.487e+00
## `myCategory.fctrStyles##Fashion:H.npnct19.log` -2.059e+00
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` -5.894e-01
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 1.100e+01
## `myCategory.fctrTStyle##:H.npnct19.log` -2.758e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.1` -4.467e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` -6.592e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` -1.855e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` -3.807e+04
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` -2.814e-02
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 3.990e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 1.542e+18
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` -4.569e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 5.074e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` -5.706e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 3.783e-02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` -8.354e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 1.693e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.183e-01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 5.313e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` -8.034e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 3.250e-04
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 3.546e+00
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` -2.347e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` -2.132e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.2` -5.290e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` -1.093e+03
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` -1.072e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 7.103e+03
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` -3.079e-02
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 2.750e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 1.004e+18
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 5.760e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 1.651e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 1.833e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 1.111e-01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 4.714e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` -7.557e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` -2.647e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` -1.071e+02
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 6.063e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` -4.110e-05
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` -8.692e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 2.201e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` -8.560e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.3` -6.727e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 2.585e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 4.405e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` -4.235e+04
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` -2.383e-02
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` -1.656e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 2.145e+18
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` -4.692e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` -1.276e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 2.207e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 3.293e-02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` -1.188e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` -2.482e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.586e-01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 1.437e+02
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 3.672e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` -2.364e-04
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` -1.425e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` -2.964e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` -1.550e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.4` -4.396e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` -9.334e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` -3.823e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 2.320e+03
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` -1.230e-02
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 2.011e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 5.934e+17
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 2.183e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` -2.375e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.070e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 6.667e-02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` -2.392e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.434e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` -1.178e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` -9.470e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` -7.166e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` -7.568e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` -9.755e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 1.022e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` -3.664e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.5` -3.762e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` -2.614e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 2.918e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` -1.066e+04
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` -3.172e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -1.720e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 1.011e+18
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` -5.718e+04
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 1.103e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` -7.956e+00
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 6.296e-03
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -1.065e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -3.540e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 5.808e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 4.815e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 2.537e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 2.098e-04
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -1.520e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` -7.330e+04
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` -5.783e+02
## `myCategory.fctr##:PubDate.wkend` -1.562e-01
## `myCategory.fctr#Multimedia#:PubDate.wkend` -1.784e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` -7.092e+00
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` NA
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 4.513e-05
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` -5.127e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` -1.321e+15
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 1.654e+00
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` -3.748e-01
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 2.515e+00
## `myCategory.fctrForeign#World#:PubDate.wkend` -1.206e-04
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` -1.667e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 9.823e-01
## `myCategory.fctrmyOther:PubDate.wkend` -6.955e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 8.211e-01
## `myCategory.fctrScience#Health#:PubDate.wkend` -1.559e-01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` -2.978e-06
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 3.060e-03
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 1.619e+01
## `myCategory.fctrTStyle##:PubDate.wkend` -1.576e+01
## `myCategory.fctr##:H.npnct28.log` 1.373e+00
## `myCategory.fctr#Multimedia#:H.npnct28.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` NA
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` NA
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 7.190e-05
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` NA
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` NA
## `myCategory.fctrCulture#Arts#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` -2.957e+01
## `myCategory.fctrmyOther:H.npnct28.log` NA
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` NA
## `myCategory.fctrScience#Health#:H.npnct28.log` NA
## `myCategory.fctrStyles##Fashion:H.npnct28.log` NA
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` NA
## `myCategory.fctrTravel#Travel#:H.npnct28.log` NA
## `myCategory.fctrTStyle##:H.npnct28.log` NA
## Std. Error
## (Intercept) 1.587e+00
## `myCategory.fctr#Multimedia#` 1.880e+04
## `myCategory.fctr#Opinion#Room For Debate` 6.074e+04
## `myCategory.fctr#Opinion#The Public Editor` 2.953e+06
## `myCategory.fctr#U.S.#Education` 7.569e+04
## `myCategory.fctrBusiness#Business Day#Dealbook` 3.587e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 2.827e+08
## `myCategory.fctrBusiness#Crosswords/Games#` 4.885e+07
## `myCategory.fctrBusiness#Technology#` 4.282e+00
## `myCategory.fctrCulture#Arts#` 3.467e+00
## `myCategory.fctrForeign#World#` 6.057e+04
## `myCategory.fctrForeign#World#Asia Pacific` 2.688e+01
## `myCategory.fctrMetro#N.Y. / Region#` 2.437e+00
## myCategory.fctrmyOther 6.169e+04
## `myCategory.fctrOpEd#Opinion#` 1.769e+00
## `myCategory.fctrScience#Health#` 2.901e+00
## `myCategory.fctrStyles##Fashion` 4.833e+04
## `myCategory.fctrStyles#U.S.#` 2.627e+00
## `myCategory.fctrTravel#Travel#` 4.273e+05
## `myCategory.fctrTStyle##` 7.124e+01
## WordCount.log.nonNA 2.322e-01
## H.npnct19.log 4.879e-01
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 2.729e+00
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 6.159e+03
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 6.894e+04
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 3.293e+03
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 5.121e-01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 2.237e+07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 2.469e+00
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 6.497e-01
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 5.677e-01
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 4.562e+03
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 1.400e+00
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 3.941e-01
## `myCategory.fctrmyOther:WordCount.log.nonNA` 7.640e+03
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.639e-01
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 4.637e-01
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 7.878e+03
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 3.930e-01
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 9.490e+03
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## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 2.260e+04
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 6.458e+04
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.037e+04
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 3.061e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 3.121e+07
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 1.817e+00
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 1.491e+00
## `myCategory.fctrForeign#World#:H.npnct19.log` 2.481e+04
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 2.661e+04
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 1.565e+00
## `myCategory.fctrmyOther:H.npnct19.log` 2.888e+04
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 9.061e-01
## `myCategory.fctrScience#Health#:H.npnct19.log` 1.229e+00
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 1.818e+04
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 1.055e+00
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 2.452e+04
## `myCategory.fctrTStyle##:H.npnct19.log` 4.221e+04
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 4.807e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 5.724e+06
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 3.529e+06
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 9.289e+08
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 1.126e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 1.878e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 1.756e+11
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 9.398e+09
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.826e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 3.601e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 1.692e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 7.547e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 5.101e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.392e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 3.993e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 5.033e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 3.415e+05
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 7.295e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 1.339e+08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 6.136e+04
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 6.244e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 3.404e+06
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 1.455e+06
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 8.543e+08
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 1.260e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 1.689e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 8.108e+10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 1.180e+10
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 1.769e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 2.947e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 9.985e+06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 5.018e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 6.078e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 6.968e+06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 6.255e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 3.018e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.700e+05
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 8.841e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 1.257e+08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 2.361e+04
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 6.454e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 5.975e+06
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 6.657e+06
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 1.036e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 1.007e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 1.665e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 2.108e+11
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 9.596e+09
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 1.646e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 3.302e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 1.959e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 9.745e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 5.643e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.847e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 8.051e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 2.837e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 4.635e+05
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 9.506e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 1.674e+08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 6.871e+04
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 4.237e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 2.218e+06
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 6.289e+06
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 3.309e+08
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 5.813e+06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.929e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 3.631e+10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 4.464e+09
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 1.781e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 3.163e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 5.179e+06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 1.652e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 4.095e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 4.419e+06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 5.569e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 4.240e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 5.379e+05
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 5.777e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 5.640e+07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 9.043e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 4.034e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 3.582e+06
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 3.182e+06
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 2.251e+08
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 1.856e+06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 1.775e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 5.856e+10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 1.167e+09
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 1.717e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 2.515e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 6.038e+06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 8.012e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.565e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 6.889e+06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 3.373e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.374e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 4.133e+05
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 6.689e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 4.109e+07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.703e+04
## `myCategory.fctr##:PubDate.wkend` 7.153e-01
## `myCategory.fctr#Multimedia#:PubDate.wkend` 1.093e+04
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 8.445e+03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` NA
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 3.094e+04
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 7.603e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` 4.899e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 3.283e+00
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 7.016e-01
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 5.822e-01
## `myCategory.fctrForeign#World#:PubDate.wkend` 6.601e+03
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 5.887e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 1.154e+00
## `myCategory.fctrmyOther:PubDate.wkend` 1.525e+04
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 4.670e-01
## `myCategory.fctrScience#Health#:PubDate.wkend` 8.183e-01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 1.222e+04
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 7.864e-01
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 7.280e+03
## `myCategory.fctrTStyle##:PubDate.wkend` 2.977e+03
## `myCategory.fctr##:H.npnct28.log` 1.671e+00
## `myCategory.fctr#Multimedia#:H.npnct28.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` NA
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` NA
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 1.154e+04
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` NA
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` NA
## `myCategory.fctrCulture#Arts#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 2.429e+04
## `myCategory.fctrmyOther:H.npnct28.log` NA
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` NA
## `myCategory.fctrScience#Health#:H.npnct28.log` NA
## `myCategory.fctrStyles##Fashion:H.npnct28.log` NA
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` NA
## `myCategory.fctrTravel#Travel#:H.npnct28.log` NA
## `myCategory.fctrTStyle##:H.npnct28.log` NA
## z value
## (Intercept) -8.588e+00
## `myCategory.fctr#Multimedia#` -1.000e-03
## `myCategory.fctr#Opinion#Room For Debate` 1.400e-02
## `myCategory.fctr#Opinion#The Public Editor` 0.000e+00
## `myCategory.fctr#U.S.#Education` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook` -2.611e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 2.222e+07
## `myCategory.fctrBusiness#Crosswords/Games#` 0.000e+00
## `myCategory.fctrBusiness#Technology#` -4.540e-01
## `myCategory.fctrCulture#Arts#` 1.729e+00
## `myCategory.fctrForeign#World#` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific` -1.009e+00
## `myCategory.fctrMetro#N.Y. / Region#` 3.743e+00
## myCategory.fctrmyOther 0.000e+00
## `myCategory.fctrOpEd#Opinion#` 7.947e+00
## `myCategory.fctrScience#Health#` 6.110e-01
## `myCategory.fctrStyles##Fashion` 0.000e+00
## `myCategory.fctrStyles#U.S.#` 3.385e+00
## `myCategory.fctrTravel#Travel#` 0.000e+00
## `myCategory.fctrTStyle##` -6.160e-01
## WordCount.log.nonNA 7.879e+00
## H.npnct19.log 4.220e+00
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` -3.940e-01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` -1.400e-02
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 0.000e+00
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` -1.000e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 2.307e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` -2.188e+07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 7.590e-01
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 5.070e-01
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` -2.141e+00
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` -1.268e+00
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` -3.568e+00
## `myCategory.fctrmyOther:WordCount.log.nonNA` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` -6.325e+00
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 6.340e-01
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 0.000e+00
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` -2.642e+00
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 9.000e-03
## `myCategory.fctrTStyle##:WordCount.log.nonNA` -2.590e-01
## `myCategory.fctr##:.clusterid.fctr2` -3.472e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 6.680e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` -5.300e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -2.740e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -2.221e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -4.820e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -1.112e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -2.960e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -2.400e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 5.760e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -4.000e-03
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` -2.530e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -4.300e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -5.180e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` -9.660e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 1.990e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.026e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.199e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` -1.133e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr4` -1.061e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 3.600e-02
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## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 4.900e-02
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` -2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` -3.500e-02
## `myCategory.fctr##:PubDate.day.minutes.poly.2` -8.470e-01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` -7.000e-03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 0.000e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 1.628e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 1.239e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 0.000e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 9.330e-01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 6.220e-01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 9.390e-01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` -1.243e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` -1.711e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 2.009e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 0.000e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` -9.830e-01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` -3.630e-01
## `myCategory.fctr##:PubDate.day.minutes.poly.3` -1.042e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 7.000e-03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 0.000e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` -9.940e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 1.018e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 0.000e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` -7.750e-01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 6.680e-01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` -1.219e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` -4.400e-01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 1.784e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 1.294e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 0.000e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` -1.500e-01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` -2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` -2.300e-02
## `myCategory.fctr##:PubDate.day.minutes.poly.4` -1.037e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` -6.000e-03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 0.000e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.043e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 1.634e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 0.000e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` -1.333e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 6.540e-01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` -1.448e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 8.390e-01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` -1.700e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` -1.690e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 0.000e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` -1.689e+00
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` -4.050e-01
## `myCategory.fctr##:PubDate.day.minutes.poly.5` -9.330e-01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 9.000e-03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 0.000e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -9.690e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 1.726e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 0.000e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 6.430e-01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` -3.160e-01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -1.330e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -9.900e-02
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 1.427e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 7.520e-01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 0.000e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -2.270e-01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` -2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` -3.400e-02
## `myCategory.fctr##:PubDate.wkend` -2.180e-01
## `myCategory.fctr#Multimedia#:PubDate.wkend` -2.000e-03
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` -1.000e-03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` NA
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` -6.740e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` -2.696e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 5.040e-01
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` -5.340e-01
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 4.321e+00
## `myCategory.fctrForeign#World#:PubDate.wkend` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` -3.000e-03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 8.510e-01
## `myCategory.fctrmyOther:PubDate.wkend` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 1.758e+00
## `myCategory.fctrScience#Health#:PubDate.wkend` -1.910e-01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 0.000e+00
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 4.000e-03
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 2.000e-03
## `myCategory.fctrTStyle##:PubDate.wkend` -5.000e-03
## `myCategory.fctr##:H.npnct28.log` 8.220e-01
## `myCategory.fctr#Multimedia#:H.npnct28.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` NA
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` NA
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` NA
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` NA
## `myCategory.fctrCulture#Arts#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` -1.000e-03
## `myCategory.fctrmyOther:H.npnct28.log` NA
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` NA
## `myCategory.fctrScience#Health#:H.npnct28.log` NA
## `myCategory.fctrStyles##Fashion:H.npnct28.log` NA
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` NA
## `myCategory.fctrTravel#Travel#:H.npnct28.log` NA
## `myCategory.fctrTStyle##:H.npnct28.log` NA
## Pr(>|z|)
## (Intercept) < 2e-16
## `myCategory.fctr#Multimedia#` 0.999595
## `myCategory.fctr#Opinion#Room For Debate` 0.988987
## `myCategory.fctr#Opinion#The Public Editor` 0.999992
## `myCategory.fctr#U.S.#Education` 0.999916
## `myCategory.fctrBusiness#Business Day#Dealbook` 0.009016
## `myCategory.fctrBusiness#Business Day#Small Business` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#` 0.999961
## `myCategory.fctrBusiness#Technology#` 0.649773
## `myCategory.fctrCulture#Arts#` 0.083745
## `myCategory.fctrForeign#World#` 0.999895
## `myCategory.fctrForeign#World#Asia Pacific` 0.313136
## `myCategory.fctrMetro#N.Y. / Region#` 0.000182
## myCategory.fctrmyOther 0.999897
## `myCategory.fctrOpEd#Opinion#` 1.91e-15
## `myCategory.fctrScience#Health#` 0.541083
## `myCategory.fctrStyles##Fashion` 0.999869
## `myCategory.fctrStyles#U.S.#` 0.000712
## `myCategory.fctrTravel#Travel#` 0.999770
## `myCategory.fctrTStyle##` 0.537937
## WordCount.log.nonNA 3.29e-15
## H.npnct19.log 2.45e-05
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 0.693860
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 0.988985
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 0.999971
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 0.999557
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 0.021074
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 0.447690
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 0.612217
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 0.032272
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 0.999680
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 0.204933
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 0.000360
## `myCategory.fctrmyOther:WordCount.log.nonNA` 0.999809
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.54e-10
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 0.525960
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 0.999815
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 0.008252
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 0.992511
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 0.795674
## `myCategory.fctr##:.clusterid.fctr2` 0.000516
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.503851
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.596021
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.784419
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.026327
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.629714
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.266248
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 0.766896
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 0.981006
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.564527
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 0.996994
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.011411
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.667289
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 0.604133
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 0.333927
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 0.842490
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 0.304722
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 0.230415
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 0.257316
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 0.288856
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 0.970954
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 0.386057
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 0.113818
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 0.805329
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 0.548224
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 0.024730
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 0.535546
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 0.951630
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 0.997908
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 0.997072
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 0.330234
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 0.079087
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.380927
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 0.785083
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 0.332370
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 0.729228
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 0.223902
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 0.752562
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 0.997397
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 0.063819
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` 0.994887
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.864938
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 0.997833
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 0.030004
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.217351
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 0.997338
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 0.773719
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 0.997050
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 0.871308
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 0.408473
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 0.902661
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 0.292265
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 0.134704
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 0.983699
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 0.998102
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 0.155777
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.997133
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 0.998347
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 0.621965
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.808700
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 0.998033
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 0.677198
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 0.798657
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 0.262505
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 0.171934
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 0.999707
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 0.999209
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 0.999842
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 0.490404
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 0.472297
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 0.934983
## `myCategory.fctrForeign#World#:H.npnct19.log` 0.999934
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 0.999753
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 0.683780
## `myCategory.fctrmyOther:H.npnct19.log` 0.999943
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 0.131099
## `myCategory.fctrScience#Health#:H.npnct19.log` 0.226250
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 0.999910
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 0.576325
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 0.999642
## `myCategory.fctrTStyle##:H.npnct19.log` 0.999479
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 0.352714
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 0.999908
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 0.995805
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 0.999967
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 0.033646
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 0.999961
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 0.781102
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 0.113068
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 0.268322
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 0.740008
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.000000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 0.183400
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 0.110450
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 1.000000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 0.961228
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 0.998602
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 0.972279
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 0.396818
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 0.999744
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 0.994122
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 0.999993
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 0.103518
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 0.999961
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 0.350649
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 0.533928
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 0.347565
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 0.213732
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 1.000000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 0.086991
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 0.044552
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 1.000000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 0.325533
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 0.998603
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 0.716946
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 0.297264
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 0.999965
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 0.994720
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 0.999967
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 0.320046
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 0.999961
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 0.438371
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 0.503836
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 0.222938
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 0.660127
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.000000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 0.074351
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 0.195578
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 1.000000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 0.880800
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 0.998587
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 0.981999
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 0.299547
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 0.999664
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 0.995149
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 0.999994
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 0.297122
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 0.999961
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 0.182415
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 0.512823
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 0.147679
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 0.401693
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.000000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 0.089043
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 0.091037
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 1.000000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 0.091289
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 0.998555
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 0.685378
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 0.351028
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 0.999942
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 0.992683
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 0.999962
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 0.332691
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 0.999961
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 0.520414
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 0.751794
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 0.183651
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 0.920903
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 1.000000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 0.153450
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 0.452040
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 1.000000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 0.820235
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 0.998577
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 0.972913
## `myCategory.fctr##:PubDate.wkend` 0.827141
## `myCategory.fctr#Multimedia#:PubDate.wkend` 0.998698
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 0.999330
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` NA
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 0.500067
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 0.614396
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 0.593198
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 1.56e-05
## `myCategory.fctrForeign#World#:PubDate.wkend` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 0.997740
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 0.394635
## `myCategory.fctrmyOther:PubDate.wkend` 1.000000
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 0.078723
## `myCategory.fctrScience#Health#:PubDate.wkend` 0.848895
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 1.000000
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 0.996895
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 0.998226
## `myCategory.fctrTStyle##:PubDate.wkend` 0.995776
## `myCategory.fctr##:H.npnct28.log` 0.411175
## `myCategory.fctr#Multimedia#:H.npnct28.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` NA
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` NA
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` NA
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` NA
## `myCategory.fctrCulture#Arts#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 0.999029
## `myCategory.fctrmyOther:H.npnct28.log` NA
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` NA
## `myCategory.fctrScience#Health#:H.npnct28.log` NA
## `myCategory.fctrStyles##Fashion:H.npnct28.log` NA
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` NA
## `myCategory.fctrTravel#Travel#:H.npnct28.log` NA
## `myCategory.fctrTStyle##:H.npnct28.log` NA
##
## (Intercept) ***
## `myCategory.fctr#Multimedia#`
## `myCategory.fctr#Opinion#Room For Debate`
## `myCategory.fctr#Opinion#The Public Editor`
## `myCategory.fctr#U.S.#Education`
## `myCategory.fctrBusiness#Business Day#Dealbook` **
## `myCategory.fctrBusiness#Business Day#Small Business` ***
## `myCategory.fctrBusiness#Crosswords/Games#`
## `myCategory.fctrBusiness#Technology#`
## `myCategory.fctrCulture#Arts#` .
## `myCategory.fctrForeign#World#`
## `myCategory.fctrForeign#World#Asia Pacific`
## `myCategory.fctrMetro#N.Y. / Region#` ***
## myCategory.fctrmyOther
## `myCategory.fctrOpEd#Opinion#` ***
## `myCategory.fctrScience#Health#`
## `myCategory.fctrStyles##Fashion`
## `myCategory.fctrStyles#U.S.#` ***
## `myCategory.fctrTravel#Travel#`
## `myCategory.fctrTStyle##`
## WordCount.log.nonNA ***
## H.npnct19.log ***
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA`
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA`
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## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` *
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` ***
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA`
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA`
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` *
## `myCategory.fctrForeign#World#:WordCount.log.nonNA`
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## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` ***
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## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` **
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## `myCategory.fctr##:.clusterid.fctr2` ***
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr4`
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## `myCategory.fctrForeign#World#:.clusterid.fctr5`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5`
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## `myCategory.fctrScience#Health#:.clusterid.fctr5`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5`
## `myCategory.fctrTStyle##:.clusterid.fctr5`
## `myCategory.fctr##:.clusterid.fctr6`
## `myCategory.fctr#Multimedia#:.clusterid.fctr6`
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr6`
## `myCategory.fctrForeign#World#:.clusterid.fctr6`
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6`
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## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6`
## `myCategory.fctrScience#Health#:.clusterid.fctr6`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6`
## `myCategory.fctrTStyle##:.clusterid.fctr6`
## `myCategory.fctr##:.clusterid.fctr7` .
## `myCategory.fctr#Multimedia#:.clusterid.fctr7`
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## `myCategory.fctr#Multimedia#:.clusterid.fctr13`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13`
## `myCategory.fctrForeign#World#:.clusterid.fctr13`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13`
## `myCategory.fctrmyOther:.clusterid.fctr13`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13`
## `myCategory.fctrScience#Health#:.clusterid.fctr13`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13`
## `myCategory.fctrTStyle##:.clusterid.fctr13`
## `myCategory.fctr##:.clusterid.fctr14`
## `myCategory.fctr#Multimedia#:.clusterid.fctr14`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14`
## `myCategory.fctrForeign#World#:.clusterid.fctr14`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14`
## `myCategory.fctrmyOther:.clusterid.fctr14`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14`
## `myCategory.fctrScience#Health#:.clusterid.fctr14`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14`
## `myCategory.fctrTStyle##:.clusterid.fctr14`
## `myCategory.fctr##:.clusterid.fctr15`
## `myCategory.fctr#Multimedia#:.clusterid.fctr15`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15`
## `myCategory.fctrForeign#World#:.clusterid.fctr15`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15`
## `myCategory.fctrmyOther:.clusterid.fctr15`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15`
## `myCategory.fctrScience#Health#:.clusterid.fctr15`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15`
## `myCategory.fctrTStyle##:.clusterid.fctr15`
## `myCategory.fctr##:.clusterid.fctr16`
## `myCategory.fctr#Multimedia#:.clusterid.fctr16`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16`
## `myCategory.fctrmyOther:.clusterid.fctr16`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16`
## `myCategory.fctrScience#Health#:.clusterid.fctr16`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16`
## `myCategory.fctrTStyle##:.clusterid.fctr16`
## `myCategory.fctr##:.clusterid.fctr17`
## `myCategory.fctr#Multimedia#:.clusterid.fctr17`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17`
## `myCategory.fctrmyOther:.clusterid.fctr17`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17`
## `myCategory.fctrScience#Health#:.clusterid.fctr17`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17`
## `myCategory.fctrTStyle##:.clusterid.fctr17`
## `myCategory.fctr#Multimedia#:H.npnct19.log`
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log`
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log`
## `myCategory.fctr#U.S.#Education:H.npnct19.log`
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log`
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` ***
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log`
## `myCategory.fctrBusiness#Technology#:H.npnct19.log`
## `myCategory.fctrCulture#Arts#:H.npnct19.log`
## `myCategory.fctrForeign#World#:H.npnct19.log`
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log`
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log`
## `myCategory.fctrmyOther:H.npnct19.log`
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log`
## `myCategory.fctrScience#Health#:H.npnct19.log`
## `myCategory.fctrStyles##Fashion:H.npnct19.log`
## `myCategory.fctrStyles#U.S.#:H.npnct19.log`
## `myCategory.fctrTravel#Travel#:H.npnct19.log`
## `myCategory.fctrTStyle##:H.npnct19.log`
## `myCategory.fctr##:PubDate.day.minutes.poly.1`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` *
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1`
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1`
## `myCategory.fctr##:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` *
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2`
## `myCategory.fctr##:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3`
## `myCategory.fctr##:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4`
## `myCategory.fctr##:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5`
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5`
## `myCategory.fctr##:PubDate.wkend`
## `myCategory.fctr#Multimedia#:PubDate.wkend`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend`
## `myCategory.fctr#U.S.#Education:PubDate.wkend`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend`
## `myCategory.fctrBusiness#Technology#:PubDate.wkend`
## `myCategory.fctrCulture#Arts#:PubDate.wkend` ***
## `myCategory.fctrForeign#World#:PubDate.wkend`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend`
## `myCategory.fctrmyOther:PubDate.wkend`
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` .
## `myCategory.fctrScience#Health#:PubDate.wkend`
## `myCategory.fctrStyles##Fashion:PubDate.wkend`
## `myCategory.fctrStyles#U.S.#:PubDate.wkend`
## `myCategory.fctrTravel#Travel#:PubDate.wkend`
## `myCategory.fctrTStyle##:PubDate.wkend`
## `myCategory.fctr##:H.npnct28.log`
## `myCategory.fctr#Multimedia#:H.npnct28.log`
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log`
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log`
## `myCategory.fctr#U.S.#Education:H.npnct28.log`
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log`
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log`
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log`
## `myCategory.fctrBusiness#Technology#:H.npnct28.log`
## `myCategory.fctrCulture#Arts#:H.npnct28.log`
## `myCategory.fctrForeign#World#:H.npnct28.log`
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log`
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log`
## `myCategory.fctrmyOther:H.npnct28.log`
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log`
## `myCategory.fctrScience#Health#:H.npnct28.log`
## `myCategory.fctrStyles##Fashion:H.npnct28.log`
## `myCategory.fctrStyles#U.S.#:H.npnct28.log`
## `myCategory.fctrTravel#Travel#:H.npnct28.log`
## `myCategory.fctrTStyle##:H.npnct28.log`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1854.3 on 4230 degrees of freedom
## AIC: 2344.3
##
## Number of Fisher Scoring iterations: 20
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.6868687
## 3 0.2 0.7462354
## 4 0.3 0.7642376
## 5 0.4 0.7634478
## 6 0.5 0.7587150
## 7 0.6 0.7373068
## 8 0.7 0.6725950
## 9 0.8 0.5964602
## 10 0.9 0.3358946
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Rank9.glm.N
## 1 N 3466
## 2 Y 125
## Popular.fctr.predict.Rank9.glm.Y
## 1 260
## 2 624
## Prediction
## Reference N Y
## N 3466 260
## Y 125 624
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.139665e-01 7.120596e-01 9.053662e-01 9.220219e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 2.036757e-56 8.534640e-12
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.6631468
## 3 0.2 0.7123947
## 4 0.3 0.7126138
## 5 0.4 0.7333333
## 6 0.5 0.7423581
## 7 0.6 0.7089783
## 8 0.7 0.6666667
## 9 0.8 0.5730337
## 10 0.9 0.3371824
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Rank9.glm.N
## 1 N 1625
## 2 Y 89
## Popular.fctr.predict.Rank9.glm.Y
## 1 88
## 2 255
## Prediction
## Reference N Y
## N 1625 88
## Y 89 255
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.139524e-01 6.907095e-01 9.009922e-01 9.257178e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 7.860332e-27 1.000000e+00
## model_id model_method
## 1 Rank9.glm glm
## feats
## 1 myCategory.fctr*WordCount.log.nonNA, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5, myCategory.fctr:PubDate.wkend, myCategory.fctr:H.npnct28.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 183.544 70.253
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9574301 0.3 0.7642376 0.8887121
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9053662 0.9220219 0.5956692 0.9337182
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7423581 0.9139524
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.9009922 0.9257178 0.6907095 2344.307
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01312544 0.0437517
## [1] "fitting model: Rank9.bayesglm"
## [1] " indep_vars: myCategory.fctr*WordCount.log.nonNA, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5, myCategory.fctr:PubDate.wkend, myCategory.fctr:H.npnct28.log"
## Aggregating results
## Fitting final model on full training set## Warning: fitted probabilities numerically 0 or 1 occurred
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3992 -0.2844 -0.1073 -0.0170 3.6048
##
## Coefficients:
## Estimate
## (Intercept) -1.200e+01
## `myCategory.fctr#Multimedia#` -1.897e-01
## `myCategory.fctr#Opinion#Room For Debate` 1.139e-01
## `myCategory.fctr#Opinion#The Public Editor` 3.083e-02
## `myCategory.fctr#U.S.#Education` -6.463e-01
## `myCategory.fctrBusiness#Business Day#Dealbook` -6.733e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 3.319e-01
## `myCategory.fctrBusiness#Crosswords/Games#` 1.122e-01
## `myCategory.fctrBusiness#Technology#` -7.864e-01
## `myCategory.fctrCulture#Arts#` 8.494e-01
## `myCategory.fctrForeign#World#` -3.055e-01
## `myCategory.fctrForeign#World#Asia Pacific` -4.553e-01
## `myCategory.fctrMetro#N.Y. / Region#` 6.064e+00
## myCategory.fctrmyOther -1.346e-01
## `myCategory.fctrOpEd#Opinion#` 1.222e+01
## `myCategory.fctrScience#Health#` 2.858e-01
## `myCategory.fctrStyles##Fashion` -3.937e-01
## `myCategory.fctrStyles#U.S.#` 5.662e+00
## `myCategory.fctrTravel#Travel#` -6.382e-01
## `myCategory.fctrTStyle##` -1.084e+00
## WordCount.log.nonNA 1.562e+00
## H.npnct19.log 1.969e+00
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` -3.595e-01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` -1.769e+00
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 6.609e-01
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` -6.107e-01
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 8.402e-01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` -9.770e-01
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 6.979e-01
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 1.713e-01
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` -3.640e-01
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` -4.701e-01
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` -9.174e-01
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` -8.962e-01
## `myCategory.fctrmyOther:WordCount.log.nonNA` -6.791e-01
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` -1.372e+00
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 5.234e-01
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` -6.858e-01
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` -5.347e-01
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` -4.258e-02
## `myCategory.fctrTStyle##:WordCount.log.nonNA` -1.089e-01
## `myCategory.fctr##:.clusterid.fctr2` -2.024e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 3.049e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` -4.004e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -3.276e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr2` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -8.438e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -2.467e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -4.930e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr2` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr3` 3.029e-02
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -8.942e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 2.789e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -1.101e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr3` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr3` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` -9.151e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -2.163e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -1.968e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr3` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr4` -9.067e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 4.981e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 5.358e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.146e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr4` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr4` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` -5.035e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` -5.516e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 3.147e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr4` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr5` -4.385e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 8.343e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -2.165e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 7.201e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr5` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr5` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` -1.025e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr5` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr5` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr6` -1.450e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -8.181e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -1.287e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -9.659e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr6` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr6` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` -4.567e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr6` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr6` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr7` -7.858e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 5.385e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 1.360e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 1.068e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr7` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr7` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 2.720e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr7` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr7` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr8` 7.141e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.500e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 0.000e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -1.060e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr8` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr8` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` -1.030e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr8` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr8` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr9` -3.327e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 9.491e-03
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -7.218e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr9` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr10` -1.775e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 7.038e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -1.406e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr10` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr11` -3.950e-02
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -1.819e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 2.875e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr11` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr12` -6.412e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` -1.258e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.097e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr12` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr13` -9.415e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -6.967e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` -6.298e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr13` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr14` -1.088e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` -2.365e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` -5.124e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr14` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr15` -1.782e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.558e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` -7.811e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr15` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr16` -3.069e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` -2.185e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 1.299e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr16` 0.000e+00
## `myCategory.fctr##:.clusterid.fctr17` -1.086e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` 0.000e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrmyOther:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` 0.000e+00
## `myCategory.fctrTStyle##:.clusterid.fctr17` 0.000e+00
## `myCategory.fctr#Multimedia#:H.npnct19.log` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` -1.144e+00
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 1.696e+00
## `myCategory.fctr#U.S.#Education:H.npnct19.log` -2.340e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` -8.891e-01
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` -1.244e-01
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 0.000e+00
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 1.033e+00
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 4.288e-01
## `myCategory.fctrForeign#World#:H.npnct19.log` -1.501e-02
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` -7.360e-02
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 5.622e-01
## `myCategory.fctrmyOther:H.npnct19.log` -5.924e-02
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` -1.208e+00
## `myCategory.fctrScience#Health#:H.npnct19.log` -1.340e+00
## `myCategory.fctrStyles##Fashion:H.npnct19.log` -1.223e-01
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` -4.320e-01
## `myCategory.fctrTravel#Travel#:H.npnct19.log` -3.807e-01
## `myCategory.fctrTStyle##:H.npnct19.log` -4.002e-01
## `myCategory.fctr##:PubDate.day.minutes.poly.1` -3.768e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` -4.157e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 1.847e+02
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 7.936e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 4.701e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 3.994e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` -4.107e+02
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 2.543e+02
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 2.529e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` -5.176e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 9.273e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 5.640e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 2.822e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 4.000e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 4.709e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` -7.548e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 2.558e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 6.084e+00
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` -1.546e+02
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` -1.030e+02
## `myCategory.fctr##:PubDate.day.minutes.poly.2` -4.588e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 3.652e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` -1.868e+02
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` -3.796e+02
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` -1.918e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 2.576e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` -1.661e+02
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 1.289e+02
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 1.487e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 1.144e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 1.901e+02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 1.727e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` -7.435e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 1.885e+02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` -9.780e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 5.728e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` -1.428e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` -9.449e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 4.241e+02
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` -8.805e+00
## `myCategory.fctr##:PubDate.day.minutes.poly.3` -5.072e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 1.104e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` -5.911e+02
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 2.349e+01
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 5.747e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` -1.468e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 1.937e+02
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` -3.280e+01
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` -1.095e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 1.521e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` -2.787e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` -3.242e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` -1.895e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.875e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 1.324e+02
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 3.427e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 1.027e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` -1.164e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 1.658e+02
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 1.085e+02
## `myCategory.fctr##:PubDate.day.minutes.poly.4` -3.736e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` -1.091e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` -4.219e+02
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 3.261e+02
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 2.471e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.908e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` -1.855e+02
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` -1.195e+02
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` -2.154e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.126e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` -2.474e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` -2.807e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.800e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.183e+02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` -8.742e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` -6.682e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 5.286e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` -1.008e+02
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 4.085e+02
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 3.440e+00
## `myCategory.fctr##:PubDate.day.minutes.poly.5` -2.677e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` -2.547e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 2.536e+02
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` -1.997e+01
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` -1.975e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -1.960e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 7.890e+02
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 1.857e+02
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 9.468e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 4.826e-02
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` -1.458e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -4.902e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -1.126e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` -3.068e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 4.409e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 2.324e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.472e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -1.605e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 4.221e+02
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 6.820e+01
## `myCategory.fctr##:PubDate.wkend` -8.592e-02
## `myCategory.fctr#Multimedia#:PubDate.wkend` -4.814e-01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` -7.104e-01
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` 0.000e+00
## `myCategory.fctr#U.S.#Education:PubDate.wkend` -1.363e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` -4.155e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` -1.838e-01
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` -4.199e-02
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` -2.248e-01
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 2.457e+00
## `myCategory.fctrForeign#World#:PubDate.wkend` -5.007e-02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` -6.442e-01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 7.095e-01
## `myCategory.fctrmyOther:PubDate.wkend` -2.224e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 7.895e-01
## `myCategory.fctrScience#Health#:PubDate.wkend` -1.197e-01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` -4.195e-02
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` -8.696e-03
## `myCategory.fctrTravel#Travel#:PubDate.wkend` -4.054e-01
## `myCategory.fctrTStyle##:PubDate.wkend` -7.597e-01
## `myCategory.fctr##:H.npnct28.log` 8.157e-01
## `myCategory.fctr#Multimedia#:H.npnct28.log` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` 0.000e+00
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` 0.000e+00
## `myCategory.fctr#U.S.#Education:H.npnct28.log` -3.974e-01
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` 0.000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` 0.000e+00
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` 0.000e+00
## `myCategory.fctrCulture#Arts#:H.npnct28.log` 0.000e+00
## `myCategory.fctrForeign#World#:H.npnct28.log` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` -1.920e+00
## `myCategory.fctrmyOther:H.npnct28.log` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` 0.000e+00
## `myCategory.fctrScience#Health#:H.npnct28.log` 0.000e+00
## `myCategory.fctrStyles##Fashion:H.npnct28.log` 0.000e+00
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` 0.000e+00
## `myCategory.fctrTravel#Travel#:H.npnct28.log` 0.000e+00
## `myCategory.fctrTStyle##:H.npnct28.log` 0.000e+00
## Std. Error
## (Intercept) 9.891e-01
## `myCategory.fctr#Multimedia#` 2.329e+00
## `myCategory.fctr#Opinion#Room For Debate` 2.460e+00
## `myCategory.fctr#Opinion#The Public Editor` 2.483e+00
## `myCategory.fctr#U.S.#Education` 2.331e+00
## `myCategory.fctrBusiness#Business Day#Dealbook` 2.311e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 2.357e+00
## `myCategory.fctrBusiness#Crosswords/Games#` 2.289e+00
## `myCategory.fctrBusiness#Technology#` 1.744e+00
## `myCategory.fctrCulture#Arts#` 1.692e+00
## `myCategory.fctrForeign#World#` 2.357e+00
## `myCategory.fctrForeign#World#Asia Pacific` 2.265e+00
## `myCategory.fctrMetro#N.Y. / Region#` 2.036e+00
## myCategory.fctrmyOther 2.448e+00
## `myCategory.fctrOpEd#Opinion#` 1.236e+00
## `myCategory.fctrScience#Health#` 1.580e+00
## `myCategory.fctrStyles##Fashion` 2.363e+00
## `myCategory.fctrStyles#U.S.#` 2.067e+00
## `myCategory.fctrTravel#Travel#` 2.420e+00
## `myCategory.fctrTStyle##` 1.805e+00
## WordCount.log.nonNA 1.480e-01
## H.npnct19.log 4.351e-01
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 7.118e-01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 1.173e+00
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 2.265e+00
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 5.149e-01
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 3.343e-01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 7.146e-01
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 5.660e-01
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 2.723e-01
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 2.834e-01
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 8.052e-01
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 5.103e-01
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 3.325e-01
## `myCategory.fctrmyOther:WordCount.log.nonNA` 1.147e+00
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 1.902e-01
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 2.658e-01
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 6.556e-01
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 3.143e-01
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 8.503e-01
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 2.853e-01
## `myCategory.fctr##:.clusterid.fctr2` 6.260e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 5.161e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 6.379e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 9.881e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr2` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 4.053e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 6.077e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 4.838e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr2` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr3` 4.394e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 6.027e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 5.148e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 1.583e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr3` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr3` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 3.909e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 5.988e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 5.117e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr3` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr4` 8.804e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 7.748e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 5.595e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 9.621e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr4` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr4` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.973e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 5.637e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 7.888e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr4` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr5` 6.579e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 5.871e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 7.709e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 1.057e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr5` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr5` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 4.831e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr5` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr5` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr6` 4.940e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 7.788e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 1.530e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.606e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr6` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr6` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 5.412e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr6` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr6` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr7` 5.693e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 7.098e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 6.933e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 1.099e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr7` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr7` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 7.260e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr7` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr7` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr8` 4.770e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 6.813e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 2.500e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 1.576e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr8` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr8` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 6.019e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr8` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr8` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr9` 1.611e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 1.000e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 1.691e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr9` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr10` 8.725e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 6.072e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 1.537e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr10` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr11` 6.123e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.471e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 1.097e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr11` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr12` 9.183e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 8.252e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.138e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr12` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr13` 8.037e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 7.848e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.749e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr13` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr14` 9.093e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 1.505e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 1.813e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr14` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr15` 8.488e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 8.399e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 1.699e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr15` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr16` 1.060e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 9.829e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 1.187e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr16` 2.500e+00
## `myCategory.fctr##:.clusterid.fctr17` 9.352e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` 2.500e+00
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrmyOther:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` 2.500e+00
## `myCategory.fctrTStyle##:.clusterid.fctr17` 2.500e+00
## `myCategory.fctr#Multimedia#:H.npnct19.log` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 2.626e+00
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 2.894e+00
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 8.784e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 2.067e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 1.614e+00
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 2.500e+00
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 1.458e+00
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 1.434e+00
## `myCategory.fctrForeign#World#:H.npnct19.log` 3.571e+00
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 3.446e+00
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 1.348e+00
## `myCategory.fctrmyOther:H.npnct19.log` 3.512e+00
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 8.458e-01
## `myCategory.fctrScience#Health#:H.npnct19.log` 1.198e+00
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 3.393e+00
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 1.034e+00
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 3.029e+00
## `myCategory.fctrTStyle##:H.npnct19.log` 2.955e+00
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 4.455e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 4.062e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 6.154e+02
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 2.726e+03
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 2.194e+02
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 1.779e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 7.041e+02
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 3.455e+02
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.753e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 3.486e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 6.608e+02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 1.382e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 5.701e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 9.819e+02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 3.792e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 4.747e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 3.044e+02
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 8.578e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 8.956e+02
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 2.346e+02
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 5.742e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 6.015e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 5.082e+02
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 2.664e+03
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 1.964e+02
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 1.615e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 8.927e+02
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 3.165e+02
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 1.723e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 2.702e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 7.757e+02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 1.223e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 7.325e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 1.534e+03
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 5.858e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 2.871e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.125e+02
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 1.052e+02
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 8.784e+02
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 2.919e+02
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 5.925e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 5.167e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 9.012e+02
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 2.598e+03
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 1.844e+02
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 1.571e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 7.399e+02
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 3.827e+02
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 1.571e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 3.183e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 7.250e+02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 1.359e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 6.783e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.326e+03
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 7.480e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 2.730e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 3.641e+02
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 1.084e+02
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 9.057e+02
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 3.099e+02
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 3.837e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 3.804e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 7.303e+02
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 2.180e+03
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 2.009e+02
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.850e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 4.753e+02
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 3.563e+02
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 1.736e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 3.120e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 5.656e+02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 1.991e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 4.736e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.012e+03
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 5.192e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 3.968e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 4.443e+02
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 6.276e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 5.268e+02
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 1.731e+02
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 3.596e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 3.738e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 6.020e+02
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 1.628e+03
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 2.152e+02
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 1.709e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 7.809e+02
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 6.056e+02
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 1.664e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 2.416e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 5.316e+02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 1.294e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.846e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 8.343e+02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 3.166e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.246e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.348e+02
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 6.808e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 5.676e+02
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.454e+02
## `myCategory.fctr##:PubDate.wkend` 6.304e-01
## `myCategory.fctr#Multimedia#:PubDate.wkend` 1.875e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 1.863e+00
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` 2.500e+00
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 2.497e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 6.753e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` 2.156e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 1.159e+00
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 6.333e-01
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 5.622e-01
## `myCategory.fctrForeign#World#:PubDate.wkend` 2.400e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 1.759e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 1.008e+00
## `myCategory.fctrmyOther:PubDate.wkend` 2.459e+00
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 4.461e-01
## `myCategory.fctrScience#Health#:PubDate.wkend` 7.427e-01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 2.418e+00
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 7.351e-01
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 2.026e+00
## `myCategory.fctrTStyle##:PubDate.wkend` 1.677e+00
## `myCategory.fctr##:H.npnct28.log` 1.482e+00
## `myCategory.fctr#Multimedia#:H.npnct28.log` 2.500e+00
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` 2.500e+00
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` 2.500e+00
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 3.145e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` 2.500e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` 2.500e+00
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` 2.500e+00
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` 2.500e+00
## `myCategory.fctrCulture#Arts#:H.npnct28.log` 2.500e+00
## `myCategory.fctrForeign#World#:H.npnct28.log` 2.500e+00
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` 2.500e+00
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 2.255e+00
## `myCategory.fctrmyOther:H.npnct28.log` 2.500e+00
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` 2.500e+00
## `myCategory.fctrScience#Health#:H.npnct28.log` 2.500e+00
## `myCategory.fctrStyles##Fashion:H.npnct28.log` 2.500e+00
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` 2.500e+00
## `myCategory.fctrTravel#Travel#:H.npnct28.log` 2.500e+00
## `myCategory.fctrTStyle##:H.npnct28.log` 2.500e+00
## z value
## (Intercept) -12.134
## `myCategory.fctr#Multimedia#` -0.081
## `myCategory.fctr#Opinion#Room For Debate` 0.046
## `myCategory.fctr#Opinion#The Public Editor` 0.012
## `myCategory.fctr#U.S.#Education` -0.277
## `myCategory.fctrBusiness#Business Day#Dealbook` -2.913
## `myCategory.fctrBusiness#Business Day#Small Business` 0.141
## `myCategory.fctrBusiness#Crosswords/Games#` 0.049
## `myCategory.fctrBusiness#Technology#` -0.451
## `myCategory.fctrCulture#Arts#` 0.502
## `myCategory.fctrForeign#World#` -0.130
## `myCategory.fctrForeign#World#Asia Pacific` -0.201
## `myCategory.fctrMetro#N.Y. / Region#` 2.978
## myCategory.fctrmyOther -0.055
## `myCategory.fctrOpEd#Opinion#` 9.881
## `myCategory.fctrScience#Health#` 0.181
## `myCategory.fctrStyles##Fashion` -0.167
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## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr15` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` 0.000
## `myCategory.fctrScience#Health#:.clusterid.fctr15` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr15` 0.000
## `myCategory.fctr##:.clusterid.fctr16` -0.290
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` -0.222
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 1.095
## `myCategory.fctrForeign#World#:.clusterid.fctr16` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr16` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` 0.000
## `myCategory.fctrScience#Health#:.clusterid.fctr16` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr16` 0.000
## `myCategory.fctr##:.clusterid.fctr17` -1.162
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` 0.000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` 0.000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` 0.000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` 0.000
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` 0.000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` 0.000
## `myCategory.fctrForeign#World#:.clusterid.fctr17` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` 0.000
## `myCategory.fctrmyOther:.clusterid.fctr17` 0.000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` 0.000
## `myCategory.fctrScience#Health#:.clusterid.fctr17` 0.000
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` 0.000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` 0.000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` 0.000
## `myCategory.fctrTStyle##:.clusterid.fctr17` 0.000
## `myCategory.fctr#Multimedia#:H.npnct19.log` 0.000
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` -0.436
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 0.586
## `myCategory.fctr#U.S.#Education:H.npnct19.log` -0.266
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` -0.430
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` -0.077
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 0.000
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 0.708
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 0.299
## `myCategory.fctrForeign#World#:H.npnct19.log` -0.004
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` -0.021
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 0.417
## `myCategory.fctrmyOther:H.npnct19.log` -0.017
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` -1.428
## `myCategory.fctrScience#Health#:H.npnct19.log` -1.118
## `myCategory.fctrStyles##Fashion:H.npnct19.log` -0.036
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` -0.418
## `myCategory.fctrTravel#Travel#:H.npnct19.log` -0.126
## `myCategory.fctrTStyle##:H.npnct19.log` -0.135
## `myCategory.fctr##:PubDate.day.minutes.poly.1` -0.846
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` -0.010
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 0.300
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 0.003
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 0.214
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 2.245
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` -0.583
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 0.736
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 0.144
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` -1.485
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 0.140
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 0.408
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 0.495
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 0.041
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.242
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` -1.590
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 0.084
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 0.071
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` -0.173
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` -0.439
## `myCategory.fctr##:PubDate.day.minutes.poly.2` -0.799
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 0.061
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` -0.368
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` -0.142
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` -0.010
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 1.595
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` -0.186
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 0.407
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 0.863
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 0.423
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 0.245
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 1.411
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` -1.015
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 0.123
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` -1.669
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 1.995
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` -0.046
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` -0.898
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 0.483
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` -0.030
## `myCategory.fctr##:PubDate.day.minutes.poly.3` -0.856
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 0.214
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` -0.656
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 0.009
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 0.031
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` -0.934
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 0.262
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` -0.086
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` -0.697
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 0.478
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` -0.038
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` -0.239
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` -0.279
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 0.014
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 1.770
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 1.256
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 0.028
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` -0.107
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 0.183
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 0.350
## `myCategory.fctr##:PubDate.day.minutes.poly.4` -0.974
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` -0.029
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` -0.578
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 0.150
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 0.012
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.031
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` -0.390
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` -0.335
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` -1.241
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 0.681
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` -0.004
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` -1.410
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 0.802
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 0.117
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` -1.684
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` -1.684
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 0.119
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` -1.607
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 0.776
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 0.020
## `myCategory.fctr##:PubDate.day.minutes.poly.5` -0.744
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` -0.068
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 0.421
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` -0.012
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` -0.092
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -1.147
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 1.010
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 0.307
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 0.569
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 0.002
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` -0.027
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -0.379
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -0.293
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` -0.037
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 1.393
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 0.716
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 0.104
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -0.236
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 0.744
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 0.469
## `myCategory.fctr##:PubDate.wkend` -0.136
## `myCategory.fctr#Multimedia#:PubDate.wkend` -0.257
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` -0.381
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` 0.000
## `myCategory.fctr#U.S.#Education:PubDate.wkend` -0.001
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` -0.615
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` -0.085
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` -0.036
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` -0.355
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 4.370
## `myCategory.fctrForeign#World#:PubDate.wkend` -0.021
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` -0.366
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 0.704
## `myCategory.fctrmyOther:PubDate.wkend` -0.009
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 1.770
## `myCategory.fctrScience#Health#:PubDate.wkend` -0.161
## `myCategory.fctrStyles##Fashion:PubDate.wkend` -0.017
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` -0.012
## `myCategory.fctrTravel#Travel#:PubDate.wkend` -0.200
## `myCategory.fctrTStyle##:PubDate.wkend` -0.453
## `myCategory.fctr##:H.npnct28.log` 0.550
## `myCategory.fctr#Multimedia#:H.npnct28.log` 0.000
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` 0.000
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` 0.000
## `myCategory.fctr#U.S.#Education:H.npnct28.log` -0.126
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` 0.000
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` 0.000
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` 0.000
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` 0.000
## `myCategory.fctrCulture#Arts#:H.npnct28.log` 0.000
## `myCategory.fctrForeign#World#:H.npnct28.log` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` -0.851
## `myCategory.fctrmyOther:H.npnct28.log` 0.000
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` 0.000
## `myCategory.fctrScience#Health#:H.npnct28.log` 0.000
## `myCategory.fctrStyles##Fashion:H.npnct28.log` 0.000
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` 0.000
## `myCategory.fctrTravel#Travel#:H.npnct28.log` 0.000
## `myCategory.fctrTStyle##:H.npnct28.log` 0.000
## Pr(>|z|)
## (Intercept) < 2e-16
## `myCategory.fctr#Multimedia#` 0.93511
## `myCategory.fctr#Opinion#Room For Debate` 0.96307
## `myCategory.fctr#Opinion#The Public Editor` 0.99010
## `myCategory.fctr#U.S.#Education` 0.78157
## `myCategory.fctrBusiness#Business Day#Dealbook` 0.00358
## `myCategory.fctrBusiness#Business Day#Small Business` 0.88801
## `myCategory.fctrBusiness#Crosswords/Games#` 0.96090
## `myCategory.fctrBusiness#Technology#` 0.65214
## `myCategory.fctrCulture#Arts#` 0.61576
## `myCategory.fctrForeign#World#` 0.89684
## `myCategory.fctrForeign#World#Asia Pacific` 0.84064
## `myCategory.fctrMetro#N.Y. / Region#` 0.00290
## myCategory.fctrmyOther 0.95615
## `myCategory.fctrOpEd#Opinion#` < 2e-16
## `myCategory.fctrScience#Health#` 0.85647
## `myCategory.fctrStyles##Fashion` 0.86768
## `myCategory.fctrStyles#U.S.#` 0.00615
## `myCategory.fctrTravel#Travel#` 0.79198
## `myCategory.fctrTStyle##` 0.54812
## WordCount.log.nonNA < 2e-16
## H.npnct19.log 6.05e-06
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 0.61353
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 0.13141
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 0.77040
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 0.23555
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 0.01195
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 0.17159
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 0.21753
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 0.52933
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 0.19905
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 0.55934
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 0.07223
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 0.00703
## `myCategory.fctrmyOther:WordCount.log.nonNA` 0.55383
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 5.60e-13
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 0.04889
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 0.29550
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 0.08884
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 0.96006
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 0.70277
## `myCategory.fctr##:.clusterid.fctr2` 0.00122
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` 1.00000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` 1.00000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` 1.00000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` 1.00000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.55467
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` 1.00000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` 1.00000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.53022
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.74024
## `myCategory.fctrForeign#World#:.clusterid.fctr2` 1.00000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` 1.00000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` 1.00000
## `myCategory.fctrmyOther:.clusterid.fctr2` 1.00000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.03735
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.68479
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` 1.00000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.30818
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` 1.00000
## `myCategory.fctrTStyle##:.clusterid.fctr2` 1.00000
## `myCategory.fctr##:.clusterid.fctr3` 0.94504
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` 1.00000
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` 1.00000
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` 1.00000
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` 1.00000
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 0.88206
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` 1.00000
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` 1.00000
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.58799
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 0.48658
## `myCategory.fctrForeign#World#:.clusterid.fctr3` 1.00000
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` 1.00000
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` 1.00000
## `myCategory.fctrmyOther:.clusterid.fctr3` 1.00000
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.01922
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.71794
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` 1.00000
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 0.70053
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` 1.00000
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## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4`
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## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4`
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## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4`
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## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5`
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## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5`
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5`
## `myCategory.fctr##:PubDate.wkend`
## `myCategory.fctr#Multimedia#:PubDate.wkend`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend`
## `myCategory.fctr#U.S.#Education:PubDate.wkend`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend`
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend`
## `myCategory.fctrBusiness#Technology#:PubDate.wkend`
## `myCategory.fctrCulture#Arts#:PubDate.wkend` ***
## `myCategory.fctrForeign#World#:PubDate.wkend`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend`
## `myCategory.fctrmyOther:PubDate.wkend`
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` .
## `myCategory.fctrScience#Health#:PubDate.wkend`
## `myCategory.fctrStyles##Fashion:PubDate.wkend`
## `myCategory.fctrStyles#U.S.#:PubDate.wkend`
## `myCategory.fctrTravel#Travel#:PubDate.wkend`
## `myCategory.fctrTStyle##:PubDate.wkend`
## `myCategory.fctr##:H.npnct28.log`
## `myCategory.fctr#Multimedia#:H.npnct28.log`
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log`
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log`
## `myCategory.fctr#U.S.#Education:H.npnct28.log`
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log`
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log`
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log`
## `myCategory.fctrBusiness#Technology#:H.npnct28.log`
## `myCategory.fctrCulture#Arts#:H.npnct28.log`
## `myCategory.fctrForeign#World#:H.npnct28.log`
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log`
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log`
## `myCategory.fctrmyOther:H.npnct28.log`
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log`
## `myCategory.fctrScience#Health#:H.npnct28.log`
## `myCategory.fctrStyles##Fashion:H.npnct28.log`
## `myCategory.fctrStyles#U.S.#:H.npnct28.log`
## `myCategory.fctrTravel#Travel#:H.npnct28.log`
## `myCategory.fctrTStyle##:H.npnct28.log`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1698.6 on 3955 degrees of freedom
## AIC: 2738.6
##
## Number of Fisher Scoring iterations: 27
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.6823869
## 3 0.2 0.7450980
## 4 0.3 0.7569919
## 5 0.4 0.7588933
## 6 0.5 0.7575758
## 7 0.6 0.7338769
## 8 0.7 0.6672078
## 9 0.8 0.5703971
## 10 0.9 0.3184855
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Rank9.bayesglm.N
## 1 N 3533
## 2 Y 173
## Popular.fctr.predict.Rank9.bayesglm.Y
## 1 193
## 2 576
## Prediction
## Reference N Y
## N 3533 193
## Y 173 576
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.182123e-01 7.096571e-01 9.097956e-01 9.260782e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 6.490343e-63 3.206389e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.6652542
## 3 0.2 0.7208738
## 4 0.3 0.7248677
## 5 0.4 0.7450425
## 6 0.5 0.7430250
## 7 0.6 0.7129338
## 8 0.7 0.6643599
## 9 0.8 0.5542636
## 10 0.9 0.3301887
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Rank9.bayesglm.N
## 1 N 1614
## 2 Y 81
## Popular.fctr.predict.Rank9.bayesglm.Y
## 1 99
## 2 263
## Prediction
## Reference N Y
## N 1614 99
## Y 81 263
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.124939e-01 6.922670e-01 8.994437e-01 9.243539e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 7.484068e-26 2.051177e-01
## model_id model_method
## 1 Rank9.bayesglm bayesglm
## feats
## 1 myCategory.fctr*WordCount.log.nonNA, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5, myCategory.fctr:PubDate.wkend, myCategory.fctr:H.npnct28.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 125.385 54.707
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9579375 0.4 0.7588933 0.9106131
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9097956 0.9260782 0.6709923 0.9420149
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7450425 0.9124939
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8994437 0.9243539 0.692267 2738.562
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.005658024 0.01718947# mdlBayesGLM <- bayesglm(reformulate(indep_vars_vctr, response=glb_rsp_var), family="binomial", data=glb_fitobs_df)
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
model_id <- "All.X.2" #"All.X.2"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
indep_vars_vctr <- setdiff(indep_vars_vctr, c(NULL
,"A.nchrs.log" # correlated to "S.*"
,"A.ndgts.log" # correlated to "S.*"
,"A.nuppr.log" # correlated to "S.*"
,"A.npnct01.log" # identical to "S.npnct01.log"
,"A.npnct03.log" # correlated to "S.npnct03.log"
,"A.npnct04.log" # correlated to "S.npnct04.log"
,"A.npnct06.log" # identical to "S.npnct06.log"
,"A.npnct07.log" # identical to "S.npnct07.log"
,"A.npnct08.log" # correlated to "S.npnct08.log"
,"A.npnct11.log" # correlated to "S.*"
,"A.npnct12.log" # correlated to "S.*"
,"S.npnct14.log" # correlated to "A.*"
,"A.npnct15.log" # correlated to "S.npnct15.log"
,"A.npnct16.log" # correlated to "S.npnct16.log"
,"A.npnct19.log" # correlated to "S.*"
,"A.npnct20.log" # identical to "S.npnct20.log"
,"A.npnct21.log" # correlated to "S.npnct21.log"
,"A.P.daily.clip.report" # identical to "S.*"
,"S.P.daily.clip.report" # identical to "H.*"
,"A.P.http" # correlated to "A.npnct14.log"
,"A.P.fashion.week" # identical to "S.*"
,"H.P.first.draft" # correlated to "H.T.first"
,"A.P.first.draft" # identical to "S.*"
,"A.P.metropolitan.diary.colon" # identical to "S.*"
,"A.P.year.colon" # identical to "S.P.year.colon"
# Following features discarded in custom model
,"H.P.readers.respond"
,"S.npnct19.log", "A.npnct19.log"
,"H.ratio.sum.TfIdf.nwrds","S.ratio.sum.TfIdf.nwrds","A.ratio.sum.TfIdf.nwrds"
,"H.npnct13.log","S.npnct13.log","A.npnct13.log"
,"H.npnct15.log","S.npnct15.log","A.npnct15.log"
,"H.ratio.nstopwrds.nwrds","S.ratio.nstopwrds.nwrds","A.ratio.nstopwrds.nwrds"
,"H.nuppr.log","S.nuppr.log","A.nuppr.log"
,"H.npnct08.log","S.npnct08.log","A.npnct08.log"
,"H.nwrds.unq.log","S.nwrds.unq.log","A.nwrds.unq.log"
,"PubDate.wkday.fctr"
,"H.npnct11.log","S.npnct11.log","A.npnct11.log"
,"H.npnct04.log","S.npnct04.log","A.npnct04.log"
,"H.nchrs.log","S.nchrs.log","A.nchrs.log"
,"H.ndgts.log","S.ndgts.log","A.ndgts.log"
,"PubDate.date.fctr","PubDate.hour.fctr"
,"H.npnct06.log","S.npnct06.log","A.npnct06.log"
,"H.npnct12.log","S.npnct12.log","A.npnct12.log"
,"H.sum.TfIdf","S.sum.TfIdf","A.sum.TfIdf"
))
indep_vars_vctr <- indep_vars_vctr[!grepl("[HSA]\\.[TP]\\.", indep_vars_vctr)]
method <- "glm"
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)## [1] "fitting model: All.X.2.glm"
## [1] " indep_vars: WordCount.log.nonNA, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, PubDate.hour.fctr.nonNA, H.npnct19.log, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, PubDate.last10.log, PubDate.last1.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, S.npnct16.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, A.npnct14.log, PubDate.minute.fctr.nonNA, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, PubDate.day.minutes.poly.5, H.npnct14.log, H.nstopwrds.log, H.npnct28.log, S.nstopwrds.log, A.nstopwrds.log, H.nwrds.log, S.nwrds.log, A.nwrds.log, myCategory.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9396 -0.3191 -0.1427 -0.0001 3.5332
##
## Coefficients: (276 not defined because of singularities)
## Estimate
## (Intercept) -2.411e+01
## WordCount.log.nonNA 1.106e+00
## myCategory.fctr.nonNA2 -8.030e-01
## myCategory.fctr.nonNA3 -3.534e+00
## myCategory.fctr.nonNA4 4.487e+00
## myCategory.fctr.nonNA5 -1.695e+01
## myCategory.fctr.nonNA6 4.006e-01
## myCategory.fctr.nonNA7 -7.228e-01
## myCategory.fctr.nonNA8 4.021e+00
## myCategory.fctr.nonNA9 1.529e+00
## myCategory.fctr.nonNA10 1.522e+00
## myCategory.fctr.nonNA11 -1.554e+01
## myCategory.fctr.nonNA12 -1.027e+00
## myCategory.fctr.nonNA13 8.082e-01
## myCategory.fctr.nonNA14 -1.671e+01
## myCategory.fctr.nonNA15 2.939e+00
## myCategory.fctr.nonNA16 3.069e+00
## myCategory.fctr.nonNA17 -1.685e+01
## myCategory.fctr.nonNA18 3.765e+00
## myCategory.fctr.nonNA19 -6.507e-01
## myCategory.fctr.nonNA20 -8.234e-01
## PubDate.day.minutes.poly.1 1.741e-01
## PubDate.hour.fctr.nonNA2 8.319e-01
## PubDate.hour.fctr.nonNA3 4.029e-01
## H.npnct19.log 1.759e+00
## PubDate.wkend -3.209e-01
## PubDate.day.minutes.poly.4 -9.290e+00
## PubDate.day.minutes.poly.2 3.485e+01
## PubDate.last10.log 6.207e-02
## PubDate.last1.log -3.491e-02
## S.npnct01.log 6.022e-01
## H.npnct16.log 8.319e-01
## PubDate.day.minutes.poly.3 5.735e+00
## H.npnct01.log -9.701e-01
## A.npnct24.log 2.577e+01
## S.npnct24.log NA
## S.npnct16.log -2.195e-01
## PubDate.last100.log 4.880e-03
## .rnorm -5.155e-02
## H.npnct05.log -2.394e+01
## PubDate.date.fctr.nonNA2 4.955e-02
## PubDate.date.fctr.nonNA3 -2.192e-01
## PubDate.date.fctr.nonNA4 -1.298e-01
## PubDate.date.fctr.nonNA5 1.609e-01
## PubDate.second.fctr.nonNA2 -5.305e-02
## PubDate.second.fctr.nonNA3 -7.912e-02
## PubDate.second.fctr.nonNA4 -3.526e-01
## H.npnct07.log 4.315e-02
## S.npnct03.log -2.743e+01
## A.npnct18.log 2.193e+00
## A.npnct02.log -1.528e+01
## A.npnct17.log 9.522e-01
## S.npnct20.log -2.385e+01
## H.npnct02.log -1.790e+01
## A.npnct14.log 4.778e-01
## PubDate.minute.fctr.nonNA2 -6.462e-03
## PubDate.minute.fctr.nonNA3 -1.859e-01
## PubDate.minute.fctr.nonNA4 7.947e-02
## PubDate.wkday.fctr.nonNA2 -6.678e-01
## PubDate.wkday.fctr.nonNA3 -1.245e+00
## PubDate.wkday.fctr.nonNA4 -8.041e-01
## PubDate.wkday.fctr.nonNA5 -1.088e+00
## PubDate.wkday.fctr.nonNA6 -1.010e+00
## PubDate.wkday.fctr.nonNA7 -9.015e-01
## S.npnct28.log -1.562e+01
## A.npnct28.log 1.997e+00
## PubDate.day.minutes.poly.5 -4.941e+00
## H.npnct14.log -2.061e+01
## H.nstopwrds.log -6.985e-02
## H.npnct28.log 5.300e-01
## S.nstopwrds.log -1.112e+01
## A.nstopwrds.log 1.207e+01
## H.nwrds.log -3.253e-01
## S.nwrds.log 1.853e+01
## A.nwrds.log -2.008e+01
## `myCategory.fctr##:.clusterid.fctr1` 1.237e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` 1.427e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` 1.989e-02
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` -1.344e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr1` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` NA
## `myCategory.fctrmyOther:.clusterid.fctr1` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 1.691e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 4.143e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` -3.198e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` NA
## `myCategory.fctrTStyle##:.clusterid.fctr1` NA
## `myCategory.fctr##:.clusterid.fctr2` -6.874e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 4.106e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` -6.529e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -2.121e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 7.776e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 2.719e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -7.462e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 1.247e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -2.733e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 5.734e-02
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -1.767e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 9.220e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.827e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -8.738e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` -8.325e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` -1.361e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 2.041e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` -8.792e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 1.009e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr4` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 4.396e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 7.445e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -7.649e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` -9.863e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 4.428e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 9.804e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -3.442e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -1.729e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -1.743e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 1.202e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` -5.735e-02
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 4.687e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` -7.870e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.526e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 1.899e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.465e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -1.723e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -1.612e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` -3.291e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -1.739e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` -9.336e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 9.802e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -1.743e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 1.035e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -1.703e+01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` -3.918e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 3.500e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 4.471e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` -5.452e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 4.218e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -1.561e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` -1.759e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` -2.063e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` -1.275e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` -1.721e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 8.410e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 2.793e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` -1.760e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 6.082e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## Std. Error
## (Intercept) 3.147e+03
## WordCount.log.nonNA 8.884e-02
## myCategory.fctr.nonNA2 1.343e+00
## myCategory.fctr.nonNA3 1.519e+00
## myCategory.fctr.nonNA4 1.551e+00
## myCategory.fctr.nonNA5 6.485e+02
## myCategory.fctr.nonNA6 1.536e+00
## myCategory.fctr.nonNA7 1.255e+00
## myCategory.fctr.nonNA8 1.189e+00
## myCategory.fctr.nonNA9 1.289e+00
## myCategory.fctr.nonNA10 1.586e+00
## myCategory.fctr.nonNA11 9.191e+02
## myCategory.fctr.nonNA12 1.262e+00
## myCategory.fctr.nonNA13 1.150e+00
## myCategory.fctr.nonNA14 1.957e+03
## myCategory.fctr.nonNA15 1.307e+00
## myCategory.fctr.nonNA16 1.197e+00
## myCategory.fctr.nonNA17 1.155e+03
## myCategory.fctr.nonNA18 1.365e+00
## myCategory.fctr.nonNA19 1.502e+00
## myCategory.fctr.nonNA20 1.188e+00
## PubDate.day.minutes.poly.1 1.586e+01
## PubDate.hour.fctr.nonNA2 4.712e-01
## PubDate.hour.fctr.nonNA3 5.418e-01
## H.npnct19.log 3.194e-01
## PubDate.wkend 4.607e-01
## PubDate.day.minutes.poly.4 9.823e+00
## PubDate.day.minutes.poly.2 1.218e+01
## PubDate.last10.log 1.294e-01
## PubDate.last1.log 4.407e-02
## S.npnct01.log 1.669e+00
## H.npnct16.log 5.632e-01
## PubDate.day.minutes.poly.3 6.497e+00
## H.npnct01.log 1.244e+00
## A.npnct24.log 4.540e+03
## S.npnct24.log NA
## S.npnct16.log 1.278e+00
## PubDate.last100.log 4.608e-02
## .rnorm 6.148e-02
## H.npnct05.log 6.073e+03
## PubDate.date.fctr.nonNA2 1.945e-01
## PubDate.date.fctr.nonNA3 1.929e-01
## PubDate.date.fctr.nonNA4 1.892e-01
## PubDate.date.fctr.nonNA5 2.033e-01
## PubDate.second.fctr.nonNA2 1.719e-01
## PubDate.second.fctr.nonNA3 1.686e-01
## PubDate.second.fctr.nonNA4 1.751e-01
## H.npnct07.log 1.981e-01
## S.npnct03.log 5.477e+03
## A.npnct18.log 2.031e+04
## A.npnct02.log 1.039e+04
## A.npnct17.log 7.103e+03
## S.npnct20.log 4.589e+03
## H.npnct02.log 3.091e+03
## A.npnct14.log 1.594e+00
## PubDate.minute.fctr.nonNA2 1.800e-01
## PubDate.minute.fctr.nonNA3 1.761e-01
## PubDate.minute.fctr.nonNA4 1.830e-01
## PubDate.wkday.fctr.nonNA2 5.432e-01
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## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 1.621e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## z value
## (Intercept) -0.008
## WordCount.log.nonNA 12.451
## myCategory.fctr.nonNA2 -0.598
## myCategory.fctr.nonNA3 -2.327
## myCategory.fctr.nonNA4 2.893
## myCategory.fctr.nonNA5 -0.026
## myCategory.fctr.nonNA6 0.261
## myCategory.fctr.nonNA7 -0.576
## myCategory.fctr.nonNA8 3.382
## myCategory.fctr.nonNA9 1.187
## myCategory.fctr.nonNA10 0.960
## myCategory.fctr.nonNA11 -0.017
## myCategory.fctr.nonNA12 -0.814
## myCategory.fctr.nonNA13 0.703
## myCategory.fctr.nonNA14 -0.009
## myCategory.fctr.nonNA15 2.249
## myCategory.fctr.nonNA16 2.564
## myCategory.fctr.nonNA17 -0.015
## myCategory.fctr.nonNA18 2.758
## myCategory.fctr.nonNA19 -0.433
## myCategory.fctr.nonNA20 -0.693
## PubDate.day.minutes.poly.1 0.011
## PubDate.hour.fctr.nonNA2 1.766
## PubDate.hour.fctr.nonNA3 0.744
## H.npnct19.log 5.508
## PubDate.wkend -0.696
## PubDate.day.minutes.poly.4 -0.946
## PubDate.day.minutes.poly.2 2.860
## PubDate.last10.log 0.480
## PubDate.last1.log -0.792
## S.npnct01.log 0.361
## H.npnct16.log 1.477
## PubDate.day.minutes.poly.3 0.883
## H.npnct01.log -0.780
## A.npnct24.log 0.006
## S.npnct24.log NA
## S.npnct16.log -0.172
## PubDate.last100.log 0.106
## .rnorm -0.838
## H.npnct05.log -0.004
## PubDate.date.fctr.nonNA2 0.255
## PubDate.date.fctr.nonNA3 -1.136
## PubDate.date.fctr.nonNA4 -0.686
## PubDate.date.fctr.nonNA5 0.792
## PubDate.second.fctr.nonNA2 -0.309
## PubDate.second.fctr.nonNA3 -0.469
## PubDate.second.fctr.nonNA4 -2.014
## H.npnct07.log 0.218
## S.npnct03.log -0.005
## A.npnct18.log 0.000
## A.npnct02.log -0.001
## A.npnct17.log 0.000
## S.npnct20.log -0.005
## H.npnct02.log -0.006
## A.npnct14.log 0.300
## PubDate.minute.fctr.nonNA2 -0.036
## PubDate.minute.fctr.nonNA3 -1.056
## PubDate.minute.fctr.nonNA4 0.434
## PubDate.wkday.fctr.nonNA2 -1.229
## PubDate.wkday.fctr.nonNA3 -2.102
## PubDate.wkday.fctr.nonNA4 -1.382
## PubDate.wkday.fctr.nonNA5 -1.880
## PubDate.wkday.fctr.nonNA6 -1.736
## PubDate.wkday.fctr.nonNA7 -1.919
## S.npnct28.log -0.001
## A.npnct28.log 0.000
## PubDate.day.minutes.poly.5 -0.744
## H.npnct14.log -0.013
## H.nstopwrds.log -0.445
## H.npnct28.log 0.333
## S.nstopwrds.log -0.519
## A.nstopwrds.log 0.563
## H.nwrds.log -1.264
## S.nwrds.log 0.757
## A.nwrds.log -0.820
## `myCategory.fctr##:.clusterid.fctr1` 1.091
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` 0.130
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` 0.026
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` -1.082
## `myCategory.fctrForeign#World#:.clusterid.fctr1` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` NA
## `myCategory.fctrmyOther:.clusterid.fctr1` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 2.326
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 0.751
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` -0.373
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` NA
## `myCategory.fctrTStyle##:.clusterid.fctr1` NA
## `myCategory.fctr##:.clusterid.fctr2` -0.543
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.358
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` -0.749
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -1.363
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.978
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.417
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -0.850
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 1.074
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -0.229
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.074
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -0.010
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 1.166
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.290
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -0.971
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` -0.552
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` -0.105
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 0.260
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` -0.717
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 1.169
## `myCategory.fctrScience#Health#:.clusterid.fctr4` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 0.345
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 0.634
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -0.745
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` -0.644
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 0.524
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 0.824
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -0.264
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -0.006
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -0.010
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 1.318
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` -0.046
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.378
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` -0.513
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.439
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 1.609
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 0.118
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -0.008
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -0.012
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` -0.221
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -0.008
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` -0.616
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.828
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -0.008
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 0.829
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -0.009
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` -0.254
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 0.230
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 0.034
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` -0.349
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 0.308
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -0.119
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` -0.006
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` -0.134
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.000
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` -0.006
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 0.620
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.209
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` -0.007
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 0.375
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## Pr(>|z|)
## (Intercept) 0.99389
## WordCount.log.nonNA < 2e-16
## myCategory.fctr.nonNA2 0.54982
## myCategory.fctr.nonNA3 0.01997
## myCategory.fctr.nonNA4 0.00382
## myCategory.fctr.nonNA5 0.97914
## myCategory.fctr.nonNA6 0.79416
## myCategory.fctr.nonNA7 0.56456
## myCategory.fctr.nonNA8 0.00072
## myCategory.fctr.nonNA9 0.23540
## myCategory.fctr.nonNA10 0.33704
## myCategory.fctr.nonNA11 0.98651
## myCategory.fctr.nonNA12 0.41575
## myCategory.fctr.nonNA13 0.48202
## myCategory.fctr.nonNA14 0.99319
## myCategory.fctr.nonNA15 0.02451
## myCategory.fctr.nonNA16 0.01036
## myCategory.fctr.nonNA17 0.98836
## myCategory.fctr.nonNA18 0.00582
## myCategory.fctr.nonNA19 0.66476
## myCategory.fctr.nonNA20 0.48820
## PubDate.day.minutes.poly.1 0.99124
## PubDate.hour.fctr.nonNA2 0.07747
## PubDate.hour.fctr.nonNA3 0.45708
## H.npnct19.log 3.63e-08
## PubDate.wkend 0.48617
## PubDate.day.minutes.poly.4 0.34430
## PubDate.day.minutes.poly.2 0.00423
## PubDate.last10.log 0.63150
## PubDate.last1.log 0.42834
## S.npnct01.log 0.71825
## H.npnct16.log 0.13962
## PubDate.day.minutes.poly.3 0.37739
## H.npnct01.log 0.43558
## A.npnct24.log 0.99547
## S.npnct24.log NA
## S.npnct16.log 0.86366
## PubDate.last100.log 0.91566
## .rnorm 0.40176
## H.npnct05.log 0.99685
## PubDate.date.fctr.nonNA2 0.79888
## PubDate.date.fctr.nonNA3 0.25591
## PubDate.date.fctr.nonNA4 0.49269
## PubDate.date.fctr.nonNA5 0.42856
## PubDate.second.fctr.nonNA2 0.75766
## PubDate.second.fctr.nonNA3 0.63878
## PubDate.second.fctr.nonNA4 0.04402
## H.npnct07.log 0.82757
## S.npnct03.log 0.99600
## A.npnct18.log 0.99991
## A.npnct02.log 0.99883
## A.npnct17.log 0.99989
## S.npnct20.log 0.99585
## H.npnct02.log 0.99538
## A.npnct14.log 0.76433
## PubDate.minute.fctr.nonNA2 0.97136
## PubDate.minute.fctr.nonNA3 0.29094
## PubDate.minute.fctr.nonNA4 0.66412
## PubDate.wkday.fctr.nonNA2 0.21889
## PubDate.wkday.fctr.nonNA3 0.03554
## PubDate.wkday.fctr.nonNA4 0.16696
## PubDate.wkday.fctr.nonNA5 0.06017
## PubDate.wkday.fctr.nonNA6 0.08257
## PubDate.wkday.fctr.nonNA7 0.05501
## S.npnct28.log 0.99951
## A.npnct28.log 0.99994
## PubDate.day.minutes.poly.5 0.45709
## H.npnct14.log 0.98956
## H.nstopwrds.log 0.65628
## H.npnct28.log 0.73944
## S.nstopwrds.log 0.60397
## A.nstopwrds.log 0.57340
## H.nwrds.log 0.20628
## S.nwrds.log 0.44927
## A.nwrds.log 0.41205
## `myCategory.fctr##:.clusterid.fctr1` 0.27530
## `myCategory.fctr#Multimedia#:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1` 0.89622
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1` 0.97888
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1` 0.27935
## `myCategory.fctrForeign#World#:.clusterid.fctr1` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1` NA
## `myCategory.fctrmyOther:.clusterid.fctr1` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` 0.02000
## `myCategory.fctrScience#Health#:.clusterid.fctr1` 0.45264
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1` 0.70878
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1` NA
## `myCategory.fctrTStyle##:.clusterid.fctr1` NA
## `myCategory.fctr##:.clusterid.fctr2` 0.58703
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.72053
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.45359
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.17281
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.32803
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.67680
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.39506
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 0.28278
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 0.81912
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.94101
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 0.99166
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.24345
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.77156
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 0.33171
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 0.58115
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 0.91654
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 0.79456
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 0.47365
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 0.24232
## `myCategory.fctrScience#Health#:.clusterid.fctr4` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 0.73011
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 0.52605
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 0.45642
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 0.51963
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 0.60017
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 0.40972
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 0.79186
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 0.99483
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 0.99175
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 0.18760
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 0.96324
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.70557
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 0.60808
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 0.15007
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 0.10757
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 0.90598
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 0.99350
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` 0.99010
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.82471
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 0.99396
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 0.53820
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.40754
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 0.99343
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 0.40732
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 0.99320
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 0.79911
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 0.81839
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 0.97287
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 0.72682
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 0.75821
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 0.90516
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 0.99483
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 0.89338
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.99965
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 0.99552
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 0.53499
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.83416
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 0.99479
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 0.70743
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
##
## (Intercept)
## WordCount.log.nonNA ***
## myCategory.fctr.nonNA2
## myCategory.fctr.nonNA3 *
## myCategory.fctr.nonNA4 **
## myCategory.fctr.nonNA5
## myCategory.fctr.nonNA6
## myCategory.fctr.nonNA7
## myCategory.fctr.nonNA8 ***
## myCategory.fctr.nonNA9
## myCategory.fctr.nonNA10
## myCategory.fctr.nonNA11
## myCategory.fctr.nonNA12
## myCategory.fctr.nonNA13
## myCategory.fctr.nonNA14
## myCategory.fctr.nonNA15 *
## myCategory.fctr.nonNA16 *
## myCategory.fctr.nonNA17
## myCategory.fctr.nonNA18 **
## myCategory.fctr.nonNA19
## myCategory.fctr.nonNA20
## PubDate.day.minutes.poly.1
## PubDate.hour.fctr.nonNA2 .
## PubDate.hour.fctr.nonNA3
## H.npnct19.log ***
## PubDate.wkend
## PubDate.day.minutes.poly.4
## PubDate.day.minutes.poly.2 **
## PubDate.last10.log
## PubDate.last1.log
## S.npnct01.log
## H.npnct16.log
## PubDate.day.minutes.poly.3
## H.npnct01.log
## A.npnct24.log
## S.npnct24.log
## S.npnct16.log
## PubDate.last100.log
## .rnorm
## H.npnct05.log
## PubDate.date.fctr.nonNA2
## PubDate.date.fctr.nonNA3
## PubDate.date.fctr.nonNA4
## PubDate.date.fctr.nonNA5
## PubDate.second.fctr.nonNA2
## PubDate.second.fctr.nonNA3
## PubDate.second.fctr.nonNA4 *
## H.npnct07.log
## S.npnct03.log
## A.npnct18.log
## A.npnct02.log
## A.npnct17.log
## S.npnct20.log
## H.npnct02.log
## A.npnct14.log
## PubDate.minute.fctr.nonNA2
## PubDate.minute.fctr.nonNA3
## PubDate.minute.fctr.nonNA4
## PubDate.wkday.fctr.nonNA2
## PubDate.wkday.fctr.nonNA3 *
## PubDate.wkday.fctr.nonNA4
## PubDate.wkday.fctr.nonNA5 .
## PubDate.wkday.fctr.nonNA6 .
## PubDate.wkday.fctr.nonNA7 .
## S.npnct28.log
## A.npnct28.log
## PubDate.day.minutes.poly.5
## H.npnct14.log
## H.nstopwrds.log
## H.npnct28.log
## S.nstopwrds.log
## A.nstopwrds.log
## H.nwrds.log
## S.nwrds.log
## A.nwrds.log
## `myCategory.fctr##:.clusterid.fctr1`
## `myCategory.fctr#Multimedia#:.clusterid.fctr1`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr1`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr1`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr1`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr1`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr1`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr1`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr1`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr1`
## `myCategory.fctrForeign#World#:.clusterid.fctr1`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr1`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr1`
## `myCategory.fctrmyOther:.clusterid.fctr1`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr1` *
## `myCategory.fctrScience#Health#:.clusterid.fctr1`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr1`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr1`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr1`
## `myCategory.fctrTStyle##:.clusterid.fctr1`
## `myCategory.fctr##:.clusterid.fctr2`
## `myCategory.fctr#Multimedia#:.clusterid.fctr2`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2`
## `myCategory.fctrForeign#World#:.clusterid.fctr2`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2`
## `myCategory.fctrmyOther:.clusterid.fctr2`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2`
## `myCategory.fctrScience#Health#:.clusterid.fctr2`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2`
## `myCategory.fctrTStyle##:.clusterid.fctr2`
## `myCategory.fctr##:.clusterid.fctr3`
## `myCategory.fctr#Multimedia#:.clusterid.fctr3`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3`
## `myCategory.fctrForeign#World#:.clusterid.fctr3`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3`
## `myCategory.fctrmyOther:.clusterid.fctr3`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3`
## `myCategory.fctrScience#Health#:.clusterid.fctr3`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3`
## `myCategory.fctrTStyle##:.clusterid.fctr3`
## `myCategory.fctr##:.clusterid.fctr4`
## `myCategory.fctr#Multimedia#:.clusterid.fctr4`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4`
## `myCategory.fctrForeign#World#:.clusterid.fctr4`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4`
## `myCategory.fctrmyOther:.clusterid.fctr4`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4`
## `myCategory.fctrScience#Health#:.clusterid.fctr4`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4`
## `myCategory.fctrTStyle##:.clusterid.fctr4`
## `myCategory.fctr##:.clusterid.fctr5`
## `myCategory.fctr#Multimedia#:.clusterid.fctr5`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5`
## `myCategory.fctrForeign#World#:.clusterid.fctr5`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5`
## `myCategory.fctrmyOther:.clusterid.fctr5`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5`
## `myCategory.fctrScience#Health#:.clusterid.fctr5`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5`
## `myCategory.fctrTStyle##:.clusterid.fctr5`
## `myCategory.fctr##:.clusterid.fctr6`
## `myCategory.fctr#Multimedia#:.clusterid.fctr6`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6`
## `myCategory.fctrForeign#World#:.clusterid.fctr6`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6`
## `myCategory.fctrmyOther:.clusterid.fctr6`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6`
## `myCategory.fctrScience#Health#:.clusterid.fctr6`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6`
## `myCategory.fctrTStyle##:.clusterid.fctr6`
## `myCategory.fctr##:.clusterid.fctr7`
## `myCategory.fctr#Multimedia#:.clusterid.fctr7`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7`
## `myCategory.fctrForeign#World#:.clusterid.fctr7`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7`
## `myCategory.fctrmyOther:.clusterid.fctr7`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7`
## `myCategory.fctrScience#Health#:.clusterid.fctr7`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7`
## `myCategory.fctrTStyle##:.clusterid.fctr7`
## `myCategory.fctr##:.clusterid.fctr8`
## `myCategory.fctr#Multimedia#:.clusterid.fctr8`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8`
## `myCategory.fctrForeign#World#:.clusterid.fctr8`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8`
## `myCategory.fctrmyOther:.clusterid.fctr8`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8`
## `myCategory.fctrScience#Health#:.clusterid.fctr8`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8`
## `myCategory.fctrTStyle##:.clusterid.fctr8`
## `myCategory.fctr##:.clusterid.fctr9`
## `myCategory.fctr#Multimedia#:.clusterid.fctr9`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9`
## `myCategory.fctrForeign#World#:.clusterid.fctr9`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9`
## `myCategory.fctrmyOther:.clusterid.fctr9`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9`
## `myCategory.fctrScience#Health#:.clusterid.fctr9`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9`
## `myCategory.fctrTStyle##:.clusterid.fctr9`
## `myCategory.fctr##:.clusterid.fctr10`
## `myCategory.fctr#Multimedia#:.clusterid.fctr10`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10`
## `myCategory.fctrForeign#World#:.clusterid.fctr10`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10`
## `myCategory.fctrmyOther:.clusterid.fctr10`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10`
## `myCategory.fctrScience#Health#:.clusterid.fctr10`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10`
## `myCategory.fctrTStyle##:.clusterid.fctr10`
## `myCategory.fctr##:.clusterid.fctr11`
## `myCategory.fctr#Multimedia#:.clusterid.fctr11`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11`
## `myCategory.fctrForeign#World#:.clusterid.fctr11`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11`
## `myCategory.fctrmyOther:.clusterid.fctr11`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11`
## `myCategory.fctrScience#Health#:.clusterid.fctr11`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11`
## `myCategory.fctrTStyle##:.clusterid.fctr11`
## `myCategory.fctr##:.clusterid.fctr12`
## `myCategory.fctr#Multimedia#:.clusterid.fctr12`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12`
## `myCategory.fctrForeign#World#:.clusterid.fctr12`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12`
## `myCategory.fctrmyOther:.clusterid.fctr12`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12`
## `myCategory.fctrScience#Health#:.clusterid.fctr12`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12`
## `myCategory.fctrTStyle##:.clusterid.fctr12`
## `myCategory.fctr##:.clusterid.fctr13`
## `myCategory.fctr#Multimedia#:.clusterid.fctr13`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13`
## `myCategory.fctrForeign#World#:.clusterid.fctr13`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13`
## `myCategory.fctrmyOther:.clusterid.fctr13`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13`
## `myCategory.fctrScience#Health#:.clusterid.fctr13`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13`
## `myCategory.fctrTStyle##:.clusterid.fctr13`
## `myCategory.fctr##:.clusterid.fctr14`
## `myCategory.fctr#Multimedia#:.clusterid.fctr14`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14`
## `myCategory.fctrForeign#World#:.clusterid.fctr14`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14`
## `myCategory.fctrmyOther:.clusterid.fctr14`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14`
## `myCategory.fctrScience#Health#:.clusterid.fctr14`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14`
## `myCategory.fctrTStyle##:.clusterid.fctr14`
## `myCategory.fctr##:.clusterid.fctr15`
## `myCategory.fctr#Multimedia#:.clusterid.fctr15`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15`
## `myCategory.fctrForeign#World#:.clusterid.fctr15`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15`
## `myCategory.fctrmyOther:.clusterid.fctr15`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15`
## `myCategory.fctrScience#Health#:.clusterid.fctr15`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15`
## `myCategory.fctrTStyle##:.clusterid.fctr15`
## `myCategory.fctr##:.clusterid.fctr16`
## `myCategory.fctr#Multimedia#:.clusterid.fctr16`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16`
## `myCategory.fctrmyOther:.clusterid.fctr16`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16`
## `myCategory.fctrScience#Health#:.clusterid.fctr16`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16`
## `myCategory.fctrTStyle##:.clusterid.fctr16`
## `myCategory.fctr##:.clusterid.fctr17`
## `myCategory.fctr#Multimedia#:.clusterid.fctr17`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17`
## `myCategory.fctrmyOther:.clusterid.fctr17`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17`
## `myCategory.fctrScience#Health#:.clusterid.fctr17`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17`
## `myCategory.fctrTStyle##:.clusterid.fctr17`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1871.0 on 4336 degrees of freedom
## AIC: 2149
##
## Number of Fisher Scoring iterations: 18
##
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor myCategory.fctr.nonNA## Warning: contrasts dropped from factor PubDate.hour.fctr.nonNA## Warning: contrasts dropped from factor PubDate.date.fctr.nonNA## Warning: contrasts dropped from factor PubDate.second.fctr.nonNA## Warning: contrasts dropped from factor PubDate.minute.fctr.nonNA## Warning: contrasts dropped from factor PubDate.wkday.fctr.nonNA## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.6579439
## 3 0.2 0.7321729
## 4 0.3 0.7435413
## 5 0.4 0.7299864
## 6 0.5 0.7131560
## 7 0.6 0.6917178
## 8 0.7 0.6429752
## 9 0.8 0.5417815
## 10 0.9 0.3861490
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.All.X.2.glm.N
## 1 N 3478
## 2 Y 159
## Popular.fctr.predict.All.X.2.glm.Y
## 1 248
## 2 590
## Prediction
## Reference N Y
## N 3478 248
## Y 159 590
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.090503e-01 6.884761e-01 9.002448e-01 9.173176e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 1.896445e-49 1.288800e-05
## [1] " calling mypredict_mdl for OOB:"## Warning: contrasts dropped from factor myCategory.fctr.nonNA## Warning: contrasts dropped from factor PubDate.hour.fctr.nonNA## Warning: contrasts dropped from factor PubDate.date.fctr.nonNA## Warning: contrasts dropped from factor PubDate.second.fctr.nonNA## Warning: contrasts dropped from factor PubDate.minute.fctr.nonNA## Warning: contrasts dropped from factor PubDate.wkday.fctr.nonNA## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.6153846
## 3 0.2 0.6975610
## 4 0.3 0.7225981
## 5 0.4 0.7159420
## 6 0.5 0.7142857
## 7 0.6 0.6866667
## 8 0.7 0.6184448
## 9 0.8 0.5212121
## 10 0.9 0.3449883
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.All.X.2.glm.N
## 1 N 1585
## 2 Y 77
## Popular.fctr.predict.All.X.2.glm.Y
## 1 128
## 2 267
## Prediction
## Reference N Y
## N 1585 128
## Y 77 267
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.003403e-01 6.622096e-01 8.865764e-01 9.129507e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 1.394337e-18 4.791464e-04
## model_id model_method
## 1 All.X.2.glm glm
## feats
## 1 WordCount.log.nonNA, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, PubDate.hour.fctr.nonNA, H.npnct19.log, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, PubDate.last10.log, PubDate.last1.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, S.npnct16.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, A.npnct14.log, PubDate.minute.fctr.nonNA, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, PubDate.day.minutes.poly.5, H.npnct14.log, H.nstopwrds.log, H.npnct28.log, S.nstopwrds.log, A.nstopwrds.log, H.nwrds.log, S.nwrds.log, A.nwrds.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 114.219 42.165
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9493305 0.3 0.7435413 0.8916157
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9002448 0.9173176 0.5854849 0.9303853
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7225981 0.9003403
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8865764 0.9129507 0.6622096 2148.969
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01728516 0.0495695#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Max.cor.Y.TmSrs.poly.glm Max.cor.Y.TmSrs.poly.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## Rank9.glm Rank9.glm glm
## Rank9.bayesglm Rank9.bayesglm bayesglm
## All.X.2.glm All.X.2.glm glm
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart A.nuppr.log, A.ratio.sum.TfIdf.nwrds
## Max.cor.Y.cv.0.cp.0.rpart A.nuppr.log, A.ratio.sum.TfIdf.nwrds
## Max.cor.Y.rpart A.nuppr.log, A.ratio.sum.TfIdf.nwrds
## Max.cor.Y.glm A.nuppr.log, A.ratio.sum.TfIdf.nwrds
## Max.cor.Y.TmSrs.poly.glm A.nuppr.log, A.ratio.sum.TfIdf.nwrds, PubDate.day.minutes.poly.1, PubDate.day.minutes.poly.2, PubDate.day.minutes.poly.3, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.5
## Interact.High.cor.Y.glm A.nuppr.log, A.ratio.sum.TfIdf.nwrds, A.nuppr.log:A.ratio.sum.TfIdf.nwrds, A.nuppr.log:S.sum.TfIdf, A.nuppr.log:PubDate.day.minutes.poly.1, A.nuppr.log:A.ratio.nstopwrds.nwrds, A.nuppr.log:S.npnct19.log, A.nuppr.log:A.npnct01.log, A.nuppr.log:H.npnct06.log, A.nuppr.log:S.npnct21.log, A.nuppr.log:A.npnct24.log, A.nuppr.log:A.npnct21.log, A.nuppr.log:A.npnct07.log, A.nuppr.log:A.npnct03.log, A.nuppr.log:A.npnct02.log, A.nuppr.log:A.npnct17.log, A.nuppr.log:A.npnct14.log, A.nuppr.log:A.npnct20.log, A.nuppr.log:A.npnct06.log, A.nuppr.log:A.npnct12.log, A.nuppr.log:A.npnct28.log, A.nuppr.log:S.npnct13.log, A.nuppr.log:A.npnct04.log, A.nuppr.log:A.npnct15.log, A.nuppr.log:A.npnct11.log, A.nuppr.log:A.nstopwrds.log, A.nuppr.log:A.ndgts.log, A.nuppr.log:H.nwrds.unq.log, A.nuppr.log:H.nchrs.log, A.nuppr.log:A.nwrds.log, A.nuppr.log:A.nchrs.log, A.nuppr.log:S.nwrds.unq.log, A.nuppr.log:A.nuppr.log
## Low.cor.X.glm A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, H.npnct19.log, A.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, A.npnct01.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, H.npnct11.log, A.npnct16.log, S.npnct16.log, S.npnct08.log, A.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, H.npnct12.log, A.npnct03.log, A.npnct20.log, H.npnct02.log, S.npnct14.log, A.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, A.npnct12.log, PubDate.wkday.fctr.nonNA, A.npnct28.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, A.npnct04.log, A.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, A.npnct11.log, A.nstopwrds.log, H.ndgts.log, A.ndgts.log, H.nwrds.unq.log, A.nuppr.log, myCategory.fctr:.clusterid.fctr
## All.X.glm A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr.nonNA, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, H.npnct11.log, S.npnct16.log, S.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, S.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, S.npnct12.log, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, A.npnct13.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, S.npnct04.log, S.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## All.X.bayesglm A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr.nonNA, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, H.npnct11.log, S.npnct16.log, S.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, S.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, S.npnct12.log, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, A.npnct13.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, S.npnct04.log, S.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr.nonNA, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, H.npnct11.log, S.npnct16.log, S.npnct08.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, S.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, S.npnct12.log, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, A.npnct13.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, S.npnct04.log, S.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## Rank9.glm myCategory.fctr*WordCount.log.nonNA, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5, myCategory.fctr:PubDate.wkend, myCategory.fctr:H.npnct28.log
## Rank9.bayesglm myCategory.fctr*WordCount.log.nonNA, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5, myCategory.fctr:PubDate.wkend, myCategory.fctr:H.npnct28.log
## All.X.2.glm WordCount.log.nonNA, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, PubDate.hour.fctr.nonNA, H.npnct19.log, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, PubDate.last10.log, PubDate.last1.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, S.npnct16.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, A.npnct14.log, PubDate.minute.fctr.nonNA, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, PubDate.day.minutes.poly.5, H.npnct14.log, H.nstopwrds.log, H.npnct28.log, S.nstopwrds.log, A.nstopwrds.log, H.nwrds.log, S.nwrds.log, A.nwrds.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO.myMFO_classfr 0 1.004
## Random.myrandom_classfr 0 0.380
## Max.cor.Y.cv.0.rpart 0 0.808
## Max.cor.Y.cv.0.cp.0.rpart 0 0.668
## Max.cor.Y.rpart 3 1.420
## Max.cor.Y.glm 1 1.343
## Max.cor.Y.TmSrs.poly.glm 1 1.558
## Interact.High.cor.Y.glm 1 2.769
## Low.cor.X.glm 1 112.177
## All.X.glm 1 112.073
## All.X.bayesglm 1 56.701
## All.X.no.rnorm.rpart 3 21.544
## Rank9.glm 1 183.544
## Rank9.bayesglm 1 125.385
## All.X.2.glm 1 114.219
## min.elapsedtime.final max.auc.fit
## MFO.myMFO_classfr 0.003 0.5000000
## Random.myrandom_classfr 0.002 0.5072166
## Max.cor.Y.cv.0.rpart 0.090 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.082 0.7768899
## Max.cor.Y.rpart 0.088 0.6939202
## Max.cor.Y.glm 0.094 0.7259296
## Max.cor.Y.TmSrs.poly.glm 0.182 0.7622760
## Interact.High.cor.Y.glm 0.756 0.8006718
## Low.cor.X.glm 42.229 0.9541851
## All.X.glm 43.228 0.9551762
## All.X.bayesglm 27.659 0.9543990
## All.X.no.rnorm.rpart 4.791 0.7281697
## Rank9.glm 70.253 0.9574301
## Rank9.bayesglm 54.707 0.9579375
## All.X.2.glm 42.165 0.9493305
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.1 0.2867534
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.3 0.4790047
## Max.cor.Y.rpart 0.2 0.4040015
## Max.cor.Y.glm 0.2 0.4159836
## Max.cor.Y.TmSrs.poly.glm 0.2 0.4482759
## Interact.High.cor.Y.glm 0.3 0.4670185
## Low.cor.X.glm 0.4 0.7451505
## All.X.glm 0.3 0.7534077
## All.X.bayesglm 0.3 0.7509340
## All.X.no.rnorm.rpart 0.7 0.5980066
## Rank9.glm 0.3 0.7642376
## Rank9.bayesglm 0.4 0.7588933
## All.X.2.glm 0.3 0.7435413
## max.Accuracy.fit max.AccuracyLower.fit
## MFO.myMFO_classfr 0.8326257 0.8213602
## Random.myrandom_classfr 0.1673743 0.1565447
## Max.cor.Y.cv.0.rpart 0.8326257 0.8213602
## Max.cor.Y.cv.0.cp.0.rpart 0.8502793 0.8394856
## Max.cor.Y.rpart 0.8444697 0.6397074
## Max.cor.Y.glm 0.8420117 0.7322161
## Max.cor.Y.TmSrs.poly.glm 0.8462566 0.7440261
## Interact.High.cor.Y.glm 0.8507265 0.8078525
## Low.cor.X.glm 0.8833492 0.9062982
## All.X.glm 0.8927341 0.9023390
## All.X.bayesglm 0.9068153 0.9018735
## All.X.no.rnorm.rpart 0.8989947 0.8823752
## Rank9.glm 0.8887121 0.9053662
## Rank9.bayesglm 0.9106131 0.9097956
## All.X.2.glm 0.8916157 0.9002448
## max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.8434553 0.0000000 0.5000000
## Random.myrandom_classfr 0.1786398 0.0000000 0.4877001
## Max.cor.Y.cv.0.rpart 0.8434553 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8606137 0.3943448 0.7031303
## Max.cor.Y.rpart 0.6678010 0.1767566 0.6818422
## Max.cor.Y.glm 0.7579659 0.1496077 0.7287696
## Max.cor.Y.TmSrs.poly.glm 0.7693808 0.2086575 0.7529375
## Interact.High.cor.Y.glm 0.8306117 0.2489790 0.7921045
## Low.cor.X.glm 0.9228764 0.5585235 0.9281045
## All.X.glm 0.9192430 0.5979193 0.9275750
## All.X.bayesglm 0.9188152 0.6466278 0.9331243
## All.X.no.rnorm.rpart 0.9007970 0.5796742 0.7084504
## Rank9.glm 0.9220219 0.5956692 0.9337182
## Rank9.bayesglm 0.9260782 0.6709923 0.9420149
## All.X.2.glm 0.9173176 0.5854849 0.9303853
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.1 0.2865473
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.2 0.4018476
## Max.cor.Y.rpart 0.2 0.3979849
## Max.cor.Y.glm 0.2 0.4115607
## Max.cor.Y.TmSrs.poly.glm 0.2 0.4284078
## Interact.High.cor.Y.glm 0.2 0.4794383
## Low.cor.X.glm 0.3 0.7081081
## All.X.glm 0.3 0.7114094
## All.X.bayesglm 0.4 0.7153285
## All.X.no.rnorm.rpart 0.7 0.5650558
## Rank9.glm 0.5 0.7423581
## Rank9.bayesglm 0.4 0.7450425
## All.X.2.glm 0.3 0.7225981
## max.Accuracy.OOB max.AccuracyLower.OOB
## MFO.myMFO_classfr 0.8327662 0.8159247
## Random.myrandom_classfr 0.1672338 0.1513467
## Max.cor.Y.cv.0.rpart 0.8327662 0.8159247
## Max.cor.Y.cv.0.cp.0.rpart 0.7481770 0.7288307
## Max.cor.Y.rpart 0.6514341 0.6303941
## Max.cor.Y.glm 0.7525523 0.7333089
## Max.cor.Y.TmSrs.poly.glm 0.7574137 0.7382877
## Interact.High.cor.Y.glm 0.7476908 0.7283332
## Low.cor.X.glm 0.8949927 0.8809341
## All.X.glm 0.8954789 0.8814466
## All.X.bayesglm 0.9052018 0.8917156
## All.X.no.rnorm.rpart 0.8862421 0.8717239
## Rank9.glm 0.9139524 0.9009922
## Rank9.bayesglm 0.9124939 0.8994437
## All.X.2.glm 0.9003403 0.8865764
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.8486533 0.0000000
## Random.myrandom_classfr 0.1840753 0.0000000
## Max.cor.Y.cv.0.rpart 0.8486533 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.7668134 0.2508040
## Max.cor.Y.rpart 0.6720416 0.2100966
## Max.cor.Y.glm 0.7710732 0.2631116
## Max.cor.Y.TmSrs.poly.glm 0.7758033 0.2831146
## Interact.High.cor.Y.glm 0.7663400 0.3334126
## Low.cor.X.glm 0.9079138 0.6444742
## All.X.glm 0.9083722 0.6480478
## All.X.bayesglm 0.9175197 0.6584619
## All.X.no.rnorm.rpart 0.8996488 0.5054039
## Rank9.glm 0.9257178 0.6907095
## Rank9.bayesglm 0.9243539 0.6922670
## All.X.2.glm 0.9129507 0.6622096
## max.AccuracySD.fit max.KappaSD.fit min.aic.fit
## MFO.myMFO_classfr NA NA NA
## Random.myrandom_classfr NA NA NA
## Max.cor.Y.cv.0.rpart NA NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA NA
## Max.cor.Y.rpart 0.002374121 0.005313439 NA
## Max.cor.Y.glm 0.003127297 0.022866369 3592.781
## Max.cor.Y.TmSrs.poly.glm 0.001742155 0.001064229 3456.116
## Interact.High.cor.Y.glm 0.003796999 0.009254505 3296.412
## Low.cor.X.glm 0.020921015 0.081233552 2092.229
## All.X.glm 0.013433120 0.051342369 2104.317
## All.X.bayesglm 0.001372296 0.001120182 2669.460
## All.X.no.rnorm.rpart 0.008429336 0.050078345 NA
## Rank9.glm 0.013125442 0.043751702 2344.307
## Rank9.bayesglm 0.005658024 0.017189472 2738.562
## All.X.2.glm 0.017285164 0.049569503 2148.969rm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 4 fit.models_1_rpart 4 0 591.029 1066.471 475.442
## 5 fit.models_1_end 5 0 1066.472 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 398.122 1066.479 668.357
## 12 fit.models 7 2 1066.479 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
# tmp_models_df <- orderBy(~model_id, glb_models_df)
# rownames(tmp_models_df) <- seq(1, nrow(tmp_models_df))
# all.equal(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr"),
# subset(stats_df, model_id != "Random.myrandom_classfr"))
# print(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
# print(subset(stats_df, model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id", grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df), grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Max.cor.Y.TmSrs.poly.glm Max.cor.Y.TmSrs.poly.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## Rank9.glm Rank9.glm glm
## Rank9.bayesglm Rank9.bayesglm bayesglm
## All.X.2.glm All.X.2.glm glm
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart A.nuppr.log, A.ratio.sum.TfIdf.nwrds
## Max.cor.Y.cv.0.cp.0.rpart A.nuppr.log, A.ratio.sum.TfIdf.nwrds
## Max.cor.Y.rpart A.nuppr.log, A.ratio.sum.TfIdf.nwrds
## Max.cor.Y.glm A.nuppr.log, A.ratio.sum.TfIdf.nwrds
## Max.cor.Y.TmSrs.poly.glm A.nuppr.log, A.ratio.sum.TfIdf.nwrds, PubDate.day.minutes.poly.1, PubDate.day.minutes.poly.2, PubDate.day.minutes.poly.3, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.5
## Interact.High.cor.Y.glm A.nuppr.log, A.ratio.sum.TfIdf.nwrds, A.nuppr.log:A.ratio.sum.TfIdf.nwrds, A.nuppr.log:S.sum.TfIdf, A.nuppr.log:PubDate.day.minutes.poly.1, A.nuppr.log:A.ratio.nstopwrds.nwrds, A.nuppr.log:S.npnct19.log, A.nuppr.log:A.npnct01.log, A.nuppr.log:H.npnct06.log, A.nuppr.log:S.npnct21.log, A.nuppr.log:A.npnct24.log, A.nuppr.log:A.npnct21.log, A.nuppr.log:A.npnct07.log, A.nuppr.log:A.npnct03.log, A.nuppr.log:A.npnct02.log, A.nuppr.log:A.npnct17.log, A.nuppr.log:A.npnct14.log, A.nuppr.log:A.npnct20.log, A.nuppr.log:A.npnct06.log, A.nuppr.log:A.npnct12.log, A.nuppr.log:A.npnct28.log, A.nuppr.log:S.npnct13.log, A.nuppr.log:A.npnct04.log, A.nuppr.log:A.npnct15.log, A.nuppr.log:A.npnct11.log, A.nuppr.log:A.nstopwrds.log, A.nuppr.log:A.ndgts.log, A.nuppr.log:H.nwrds.unq.log, A.nuppr.log:H.nchrs.log, A.nuppr.log:A.nwrds.log, A.nuppr.log:A.nchrs.log, A.nuppr.log:S.nwrds.unq.log, A.nuppr.log:A.nuppr.log
## Low.cor.X.glm A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, H.npnct19.log, A.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, A.npnct01.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, H.npnct11.log, A.npnct16.log, S.npnct16.log, S.npnct08.log, A.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, H.npnct12.log, A.npnct03.log, A.npnct20.log, H.npnct02.log, S.npnct14.log, A.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, A.npnct12.log, PubDate.wkday.fctr.nonNA, A.npnct28.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, A.npnct04.log, A.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, A.npnct11.log, A.nstopwrds.log, H.ndgts.log, A.ndgts.log, H.nwrds.unq.log, A.nuppr.log, myCategory.fctr:.clusterid.fctr
## All.X.glm A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr.nonNA, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, H.npnct11.log, S.npnct16.log, S.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, S.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, S.npnct12.log, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, A.npnct13.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, S.npnct04.log, S.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## All.X.bayesglm A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr.nonNA, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, H.npnct11.log, S.npnct16.log, S.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, S.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, S.npnct12.log, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, A.npnct13.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, S.npnct04.log, S.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart A.ratio.sum.TfIdf.nwrds, WordCount.log.nonNA, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr.nonNA, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, S.npnct19.log, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.ratio.nstopwrds.nwrds, H.npnct06.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, H.npnct11.log, S.npnct16.log, S.npnct08.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, S.npnct06.log, A.npnct14.log, H.npnct13.log, PubDate.minute.fctr.nonNA, S.npnct12.log, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, A.npnct13.log, H.npnct04.log, S.npnct13.log, PubDate.day.minutes.poly.5, H.npnct14.log, S.npnct04.log, S.npnct15.log, H.npnct15.log, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## Rank9.glm myCategory.fctr*WordCount.log.nonNA, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5, myCategory.fctr:PubDate.wkend, myCategory.fctr:H.npnct28.log
## Rank9.bayesglm myCategory.fctr*WordCount.log.nonNA, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5, myCategory.fctr:PubDate.wkend, myCategory.fctr:H.npnct28.log
## All.X.2.glm WordCount.log.nonNA, myCategory.fctr.nonNA, PubDate.day.minutes.poly.1, PubDate.hour.fctr.nonNA, H.npnct19.log, PubDate.wkend, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, PubDate.last10.log, PubDate.last1.log, S.npnct01.log, H.npnct16.log, PubDate.day.minutes.poly.3, H.npnct01.log, A.npnct24.log, S.npnct24.log, S.npnct16.log, PubDate.last100.log, .rnorm, H.npnct05.log, PubDate.date.fctr.nonNA, PubDate.second.fctr.nonNA, H.npnct07.log, S.npnct03.log, A.npnct18.log, A.npnct02.log, A.npnct17.log, S.npnct20.log, H.npnct02.log, A.npnct14.log, PubDate.minute.fctr.nonNA, PubDate.wkday.fctr.nonNA, S.npnct28.log, A.npnct28.log, PubDate.day.minutes.poly.5, H.npnct14.log, H.nstopwrds.log, H.npnct28.log, S.nstopwrds.log, A.nstopwrds.log, H.nwrds.log, S.nwrds.log, A.nwrds.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns max.auc.fit
## MFO.myMFO_classfr 0 0.5000000
## Random.myrandom_classfr 0 0.5072166
## Max.cor.Y.cv.0.rpart 0 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.7768899
## Max.cor.Y.rpart 3 0.6939202
## Max.cor.Y.glm 1 0.7259296
## Max.cor.Y.TmSrs.poly.glm 1 0.7622760
## Interact.High.cor.Y.glm 1 0.8006718
## Low.cor.X.glm 1 0.9541851
## All.X.glm 1 0.9551762
## All.X.bayesglm 1 0.9543990
## All.X.no.rnorm.rpart 3 0.7281697
## Rank9.glm 1 0.9574301
## Rank9.bayesglm 1 0.9579375
## All.X.2.glm 1 0.9493305
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.1 0.2867534
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.3 0.4790047
## Max.cor.Y.rpart 0.2 0.4040015
## Max.cor.Y.glm 0.2 0.4159836
## Max.cor.Y.TmSrs.poly.glm 0.2 0.4482759
## Interact.High.cor.Y.glm 0.3 0.4670185
## Low.cor.X.glm 0.4 0.7451505
## All.X.glm 0.3 0.7534077
## All.X.bayesglm 0.3 0.7509340
## All.X.no.rnorm.rpart 0.7 0.5980066
## Rank9.glm 0.3 0.7642376
## Rank9.bayesglm 0.4 0.7588933
## All.X.2.glm 0.3 0.7435413
## max.Accuracy.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.8326257 0.0000000 0.5000000
## Random.myrandom_classfr 0.1673743 0.0000000 0.4877001
## Max.cor.Y.cv.0.rpart 0.8326257 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8502793 0.3943448 0.7031303
## Max.cor.Y.rpart 0.8444697 0.1767566 0.6818422
## Max.cor.Y.glm 0.8420117 0.1496077 0.7287696
## Max.cor.Y.TmSrs.poly.glm 0.8462566 0.2086575 0.7529375
## Interact.High.cor.Y.glm 0.8507265 0.2489790 0.7921045
## Low.cor.X.glm 0.8833492 0.5585235 0.9281045
## All.X.glm 0.8927341 0.5979193 0.9275750
## All.X.bayesglm 0.9068153 0.6466278 0.9331243
## All.X.no.rnorm.rpart 0.8989947 0.5796742 0.7084504
## Rank9.glm 0.8887121 0.5956692 0.9337182
## Rank9.bayesglm 0.9106131 0.6709923 0.9420149
## All.X.2.glm 0.8916157 0.5854849 0.9303853
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.1 0.2865473
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.2 0.4018476
## Max.cor.Y.rpart 0.2 0.3979849
## Max.cor.Y.glm 0.2 0.4115607
## Max.cor.Y.TmSrs.poly.glm 0.2 0.4284078
## Interact.High.cor.Y.glm 0.2 0.4794383
## Low.cor.X.glm 0.3 0.7081081
## All.X.glm 0.3 0.7114094
## All.X.bayesglm 0.4 0.7153285
## All.X.no.rnorm.rpart 0.7 0.5650558
## Rank9.glm 0.5 0.7423581
## Rank9.bayesglm 0.4 0.7450425
## All.X.2.glm 0.3 0.7225981
## max.Accuracy.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.8327662 0.0000000
## Random.myrandom_classfr 0.1672338 0.0000000
## Max.cor.Y.cv.0.rpart 0.8327662 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.7481770 0.2508040
## Max.cor.Y.rpart 0.6514341 0.2100966
## Max.cor.Y.glm 0.7525523 0.2631116
## Max.cor.Y.TmSrs.poly.glm 0.7574137 0.2831146
## Interact.High.cor.Y.glm 0.7476908 0.3334126
## Low.cor.X.glm 0.8949927 0.6444742
## All.X.glm 0.8954789 0.6480478
## All.X.bayesglm 0.9052018 0.6584619
## All.X.no.rnorm.rpart 0.8862421 0.5054039
## Rank9.glm 0.9139524 0.6907095
## Rank9.bayesglm 0.9124939 0.6922670
## All.X.2.glm 0.9003403 0.6622096
## inv.elapsedtime.everything inv.elapsedtime.final
## MFO.myMFO_classfr 0.996015936 333.33333333
## Random.myrandom_classfr 2.631578947 500.00000000
## Max.cor.Y.cv.0.rpart 1.237623762 11.11111111
## Max.cor.Y.cv.0.cp.0.rpart 1.497005988 12.19512195
## Max.cor.Y.rpart 0.704225352 11.36363636
## Max.cor.Y.glm 0.744601638 10.63829787
## Max.cor.Y.TmSrs.poly.glm 0.641848524 5.49450549
## Interact.High.cor.Y.glm 0.361141206 1.32275132
## Low.cor.X.glm 0.008914483 0.02368041
## All.X.glm 0.008922756 0.02313315
## All.X.bayesglm 0.017636373 0.03615460
## All.X.no.rnorm.rpart 0.046416636 0.20872469
## Rank9.glm 0.005448285 0.01423427
## Rank9.bayesglm 0.007975436 0.01827920
## All.X.2.glm 0.008755111 0.02371635
## inv.aic.fit
## MFO.myMFO_classfr NA
## Random.myrandom_classfr NA
## Max.cor.Y.cv.0.rpart NA
## Max.cor.Y.cv.0.cp.0.rpart NA
## Max.cor.Y.rpart NA
## Max.cor.Y.glm 0.0002783359
## Max.cor.Y.TmSrs.poly.glm 0.0002893422
## Interact.High.cor.Y.glm 0.0003033601
## Low.cor.X.glm 0.0004779592
## All.X.glm 0.0004752136
## All.X.bayesglm 0.0003746076
## All.X.no.rnorm.rpart NA
## Rank9.glm 0.0004265652
## Rank9.bayesglm 0.0003651552
## All.X.2.glm 0.0004653393print(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 15. Consider specifying shapes manually. if you must have them.## Warning in loop_apply(n, do.ply): Removed 3 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 131 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 15. Consider specifying shapes manually. if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
#print(mltdCI_models_df)
# castCI_models_df <- dcast(mltdCI_models_df, value ~ type, fun.aggregate=sum)
# print(castCI_models_df)
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
dev.off()## quartz_off_screen
## 2print(gp)
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id max.Accuracy.OOB
## Rank9.glm Rank9.glm 0.9139524
## Rank9.bayesglm Rank9.bayesglm 0.9124939
## All.X.bayesglm All.X.bayesglm 0.9052018
## All.X.2.glm All.X.2.glm 0.9003403
## All.X.glm All.X.glm 0.8954789
## Low.cor.X.glm Low.cor.X.glm 0.8949927
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart 0.8862421
## MFO.myMFO_classfr MFO.myMFO_classfr 0.8327662
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart 0.8327662
## Max.cor.Y.TmSrs.poly.glm Max.cor.Y.TmSrs.poly.glm 0.7574137
## Max.cor.Y.glm Max.cor.Y.glm 0.7525523
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart 0.7481770
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm 0.7476908
## Max.cor.Y.rpart Max.cor.Y.rpart 0.6514341
## Random.myrandom_classfr Random.myrandom_classfr 0.1672338
## max.auc.OOB max.Kappa.OOB min.aic.fit
## Rank9.glm 0.9337182 0.6907095 2344.307
## Rank9.bayesglm 0.9420149 0.6922670 2738.562
## All.X.bayesglm 0.9331243 0.6584619 2669.460
## All.X.2.glm 0.9303853 0.6622096 2148.969
## All.X.glm 0.9275750 0.6480478 2104.317
## Low.cor.X.glm 0.9281045 0.6444742 2092.229
## All.X.no.rnorm.rpart 0.7084504 0.5054039 NA
## MFO.myMFO_classfr 0.5000000 0.0000000 NA
## Max.cor.Y.cv.0.rpart 0.5000000 0.0000000 NA
## Max.cor.Y.TmSrs.poly.glm 0.7529375 0.2831146 3456.116
## Max.cor.Y.glm 0.7287696 0.2631116 3592.781
## Max.cor.Y.cv.0.cp.0.rpart 0.7031303 0.2508040 NA
## Interact.High.cor.Y.glm 0.7921045 0.3334126 3296.412
## Max.cor.Y.rpart 0.6818422 0.2100966 NA
## Random.myrandom_classfr 0.4877001 0.0000000 NA
## opt.prob.threshold.OOB
## Rank9.glm 0.5
## Rank9.bayesglm 0.4
## All.X.bayesglm 0.4
## All.X.2.glm 0.3
## All.X.glm 0.3
## Low.cor.X.glm 0.3
## All.X.no.rnorm.rpart 0.7
## MFO.myMFO_classfr 0.5
## Max.cor.Y.cv.0.rpart 0.5
## Max.cor.Y.TmSrs.poly.glm 0.2
## Max.cor.Y.glm 0.2
## Max.cor.Y.cv.0.cp.0.rpart 0.2
## Interact.High.cor.Y.glm 0.2
## Max.cor.Y.rpart 0.2
## Random.myrandom_classfr 0.1print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 15. Consider specifying shapes manually. if you must have them.## Warning in loop_apply(n, do.ply): Removed 55 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 15. Consider specifying shapes manually. if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.auc.OOB - max.Kappa.OOB + min.aic.fit -
## opt.prob.threshold.OOBprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: Rank9.glm"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
}
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])## Warning: not plotting observations with leverage one:
## 121, 2403
## Warning: not plotting observations with leverage one:
## 121, 2403
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4280 -0.2438 -0.0387 0.0000 8.4904
##
## Coefficients: (275 not defined because of singularities)
## Estimate
## (Intercept) -1.363e+01
## `myCategory.fctr#Multimedia#` -9.548e+00
## `myCategory.fctr#Opinion#Room For Debate` 8.385e+02
## `myCategory.fctr#Opinion#The Public Editor` 3.115e+01
## `myCategory.fctr#U.S.#Education` -7.941e+00
## `myCategory.fctrBusiness#Business Day#Dealbook` -9.367e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 6.282e+15
## `myCategory.fctrBusiness#Crosswords/Games#` 2.371e+03
## `myCategory.fctrBusiness#Technology#` -1.944e+00
## `myCategory.fctrCulture#Arts#` 5.996e+00
## `myCategory.fctrForeign#World#` -7.941e+00
## `myCategory.fctrForeign#World#Asia Pacific` -2.712e+01
## `myCategory.fctrMetro#N.Y. / Region#` 9.122e+00
## myCategory.fctrmyOther -7.941e+00
## `myCategory.fctrOpEd#Opinion#` 1.406e+01
## `myCategory.fctrScience#Health#` 1.773e+00
## `myCategory.fctrStyles##Fashion` -7.941e+00
## `myCategory.fctrStyles#U.S.#` 8.893e+00
## `myCategory.fctrTravel#Travel#` 1.231e+02
## `myCategory.fctrTStyle##` -4.388e+01
## WordCount.log.nonNA 1.829e+00
## H.npnct19.log 2.059e+00
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` -1.074e+00
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` -8.504e+01
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` -2.502e+00
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` -1.829e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 1.181e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` -4.894e+14
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 1.875e+00
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 3.293e-01
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` -1.215e+00
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` -1.829e+00
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` -1.775e+00
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` -1.406e+00
## `myCategory.fctrmyOther:WordCount.log.nonNA` -1.829e+00
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` -1.669e+00
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 2.941e-01
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` -1.829e+00
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` -1.038e+00
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 8.907e+01
## `myCategory.fctrTStyle##:WordCount.log.nonNA` -1.614e-01
## `myCategory.fctr##:.clusterid.fctr2` -2.426e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 3.818e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` -3.871e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -3.392e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -9.280e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -3.170e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -5.621e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -1.432e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -1.600e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 3.262e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -1.767e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` -1.020e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -2.810e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -2.782e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` -1.076e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 1.774e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 6.479e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.440e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` -5.855e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` -6.425e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 3.110e-02
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` -6.432e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 1.021e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -2.175e-01
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## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` -3.079e-02
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 2.750e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 1.004e+18
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 5.760e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 1.651e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 1.833e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 1.111e-01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 4.714e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` -7.557e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` -2.647e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` -1.071e+02
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 6.063e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` -4.110e-05
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` -8.692e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 2.201e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` -8.560e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.3` -6.727e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 2.585e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 4.405e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` -4.235e+04
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` -2.383e-02
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` -1.656e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 2.145e+18
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` -4.692e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` -1.276e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 2.207e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 3.293e-02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` -1.188e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` -2.482e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.586e-01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 1.437e+02
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 3.672e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` -2.364e-04
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` -1.425e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` -2.964e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` -1.550e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.4` -4.396e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` -9.334e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` -3.823e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 2.320e+03
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` -1.230e-02
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 2.011e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 5.934e+17
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 2.183e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` -2.375e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.070e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 6.667e-02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` -2.392e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.434e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` -1.178e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` -9.470e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` -7.166e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` -7.568e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` -9.755e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 1.022e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` -3.664e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.5` -3.762e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` -2.614e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 2.918e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` -1.066e+04
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` -3.172e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -1.720e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 1.011e+18
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` -5.718e+04
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 1.103e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` -7.956e+00
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 6.296e-03
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -1.065e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -3.540e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 5.808e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 4.815e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 2.537e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 2.098e-04
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -1.520e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` -7.330e+04
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` -5.783e+02
## `myCategory.fctr##:PubDate.wkend` -1.562e-01
## `myCategory.fctr#Multimedia#:PubDate.wkend` -1.784e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` -7.092e+00
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` NA
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 4.513e-05
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` -5.127e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` -1.321e+15
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 1.654e+00
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` -3.748e-01
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 2.515e+00
## `myCategory.fctrForeign#World#:PubDate.wkend` -1.206e-04
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` -1.667e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 9.823e-01
## `myCategory.fctrmyOther:PubDate.wkend` -6.955e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 8.211e-01
## `myCategory.fctrScience#Health#:PubDate.wkend` -1.559e-01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` -2.978e-06
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 3.060e-03
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 1.619e+01
## `myCategory.fctrTStyle##:PubDate.wkend` -1.576e+01
## `myCategory.fctr##:H.npnct28.log` 1.373e+00
## `myCategory.fctr#Multimedia#:H.npnct28.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` NA
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` NA
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 7.190e-05
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` NA
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` NA
## `myCategory.fctrCulture#Arts#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` -2.957e+01
## `myCategory.fctrmyOther:H.npnct28.log` NA
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` NA
## `myCategory.fctrScience#Health#:H.npnct28.log` NA
## `myCategory.fctrStyles##Fashion:H.npnct28.log` NA
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` NA
## `myCategory.fctrTravel#Travel#:H.npnct28.log` NA
## `myCategory.fctrTStyle##:H.npnct28.log` NA
## Std. Error
## (Intercept) 1.587e+00
## `myCategory.fctr#Multimedia#` 1.880e+04
## `myCategory.fctr#Opinion#Room For Debate` 6.074e+04
## `myCategory.fctr#Opinion#The Public Editor` 2.953e+06
## `myCategory.fctr#U.S.#Education` 7.569e+04
## `myCategory.fctrBusiness#Business Day#Dealbook` 3.587e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 2.827e+08
## `myCategory.fctrBusiness#Crosswords/Games#` 4.885e+07
## `myCategory.fctrBusiness#Technology#` 4.282e+00
## `myCategory.fctrCulture#Arts#` 3.467e+00
## `myCategory.fctrForeign#World#` 6.057e+04
## `myCategory.fctrForeign#World#Asia Pacific` 2.688e+01
## `myCategory.fctrMetro#N.Y. / Region#` 2.437e+00
## myCategory.fctrmyOther 6.169e+04
## `myCategory.fctrOpEd#Opinion#` 1.769e+00
## `myCategory.fctrScience#Health#` 2.901e+00
## `myCategory.fctrStyles##Fashion` 4.833e+04
## `myCategory.fctrStyles#U.S.#` 2.627e+00
## `myCategory.fctrTravel#Travel#` 4.273e+05
## `myCategory.fctrTStyle##` 7.124e+01
## WordCount.log.nonNA 2.322e-01
## H.npnct19.log 4.879e-01
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 2.729e+00
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 6.159e+03
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 6.894e+04
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 3.293e+03
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 5.121e-01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 2.237e+07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 2.469e+00
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 6.497e-01
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 5.677e-01
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 4.562e+03
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 1.400e+00
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 3.941e-01
## `myCategory.fctrmyOther:WordCount.log.nonNA` 7.640e+03
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.639e-01
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 4.637e-01
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 7.878e+03
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 3.930e-01
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 9.490e+03
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 6.233e-01
## `myCategory.fctr##:.clusterid.fctr2` 6.988e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 5.712e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 7.302e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 1.240e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 4.178e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 6.575e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 5.056e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 4.831e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 6.719e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 5.661e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 4.689e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 4.032e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 6.538e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 5.366e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 1.113e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 8.928e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 6.313e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.201e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 5.169e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 6.057e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 8.540e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 7.420e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 6.456e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 8.826e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 1.224e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 4.997e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 5.457e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 9.050e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 7.061e+03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 4.719e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 5.647e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 6.305e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 8.043e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 7.735e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 1.229e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 8.036e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 5.185e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 7.536e-01
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## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 1.652e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 4.095e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 4.419e+06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 5.569e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 4.240e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 5.379e+05
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 5.777e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 5.640e+07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 9.043e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 4.034e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 3.582e+06
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 3.182e+06
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 2.251e+08
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 1.856e+06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 1.775e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 5.856e+10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 1.167e+09
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 1.717e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 2.515e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 6.038e+06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 8.012e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.565e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 6.889e+06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 3.373e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.374e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 4.133e+05
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 6.689e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 4.109e+07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.703e+04
## `myCategory.fctr##:PubDate.wkend` 7.153e-01
## `myCategory.fctr#Multimedia#:PubDate.wkend` 1.093e+04
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 8.445e+03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` NA
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 3.094e+04
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 7.603e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` 4.899e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 3.283e+00
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 7.016e-01
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 5.822e-01
## `myCategory.fctrForeign#World#:PubDate.wkend` 6.601e+03
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 5.887e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 1.154e+00
## `myCategory.fctrmyOther:PubDate.wkend` 1.525e+04
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 4.670e-01
## `myCategory.fctrScience#Health#:PubDate.wkend` 8.183e-01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 1.222e+04
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 7.864e-01
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 7.280e+03
## `myCategory.fctrTStyle##:PubDate.wkend` 2.977e+03
## `myCategory.fctr##:H.npnct28.log` 1.671e+00
## `myCategory.fctr#Multimedia#:H.npnct28.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` NA
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` NA
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 1.154e+04
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` NA
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` NA
## `myCategory.fctrCulture#Arts#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 2.429e+04
## `myCategory.fctrmyOther:H.npnct28.log` NA
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` NA
## `myCategory.fctrScience#Health#:H.npnct28.log` NA
## `myCategory.fctrStyles##Fashion:H.npnct28.log` NA
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` NA
## `myCategory.fctrTravel#Travel#:H.npnct28.log` NA
## `myCategory.fctrTStyle##:H.npnct28.log` NA
## z value
## (Intercept) -8.588e+00
## `myCategory.fctr#Multimedia#` -1.000e-03
## `myCategory.fctr#Opinion#Room For Debate` 1.400e-02
## `myCategory.fctr#Opinion#The Public Editor` 0.000e+00
## `myCategory.fctr#U.S.#Education` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook` -2.611e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 2.222e+07
## `myCategory.fctrBusiness#Crosswords/Games#` 0.000e+00
## `myCategory.fctrBusiness#Technology#` -4.540e-01
## `myCategory.fctrCulture#Arts#` 1.729e+00
## `myCategory.fctrForeign#World#` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific` -1.009e+00
## `myCategory.fctrMetro#N.Y. / Region#` 3.743e+00
## myCategory.fctrmyOther 0.000e+00
## `myCategory.fctrOpEd#Opinion#` 7.947e+00
## `myCategory.fctrScience#Health#` 6.110e-01
## `myCategory.fctrStyles##Fashion` 0.000e+00
## `myCategory.fctrStyles#U.S.#` 3.385e+00
## `myCategory.fctrTravel#Travel#` 0.000e+00
## `myCategory.fctrTStyle##` -6.160e-01
## WordCount.log.nonNA 7.879e+00
## H.npnct19.log 4.220e+00
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` -3.940e-01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` -1.400e-02
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 0.000e+00
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` -1.000e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 2.307e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` -2.188e+07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 7.590e-01
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 5.070e-01
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` -2.141e+00
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` -1.268e+00
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` -3.568e+00
## `myCategory.fctrmyOther:WordCount.log.nonNA` 0.000e+00
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` -6.325e+00
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 6.340e-01
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 0.000e+00
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` -2.642e+00
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 9.000e-03
## `myCategory.fctrTStyle##:WordCount.log.nonNA` -2.590e-01
## `myCategory.fctr##:.clusterid.fctr2` -3.472e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 6.680e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` -5.300e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -2.740e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -2.221e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -4.820e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -1.112e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -2.960e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -2.400e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 5.760e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -4.000e-03
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` -2.530e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -4.300e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -5.180e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` -9.660e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 1.990e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.026e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.199e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` -1.133e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr4` -1.061e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 3.600e-02
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` -8.670e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 1.581e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -2.460e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 6.000e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` -2.246e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` -6.200e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 6.100e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -3.000e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -4.000e-03
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` -9.740e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` -1.756e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 8.760e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 2.730e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 9.690e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 3.460e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 1.216e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 3.150e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -3.000e-03
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` -1.853e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -6.000e-03
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 1.700e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -3.000e-03
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` -2.170e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 1.234e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -3.000e-03
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` -2.880e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -4.000e-03
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## `myCategory.fctr#Multimedia#:H.npnct28.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` NA
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` NA
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` NA
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` NA
## `myCategory.fctrCulture#Arts#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` -1.000e-03
## `myCategory.fctrmyOther:H.npnct28.log` NA
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` NA
## `myCategory.fctrScience#Health#:H.npnct28.log` NA
## `myCategory.fctrStyles##Fashion:H.npnct28.log` NA
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` NA
## `myCategory.fctrTravel#Travel#:H.npnct28.log` NA
## `myCategory.fctrTStyle##:H.npnct28.log` NA
## Pr(>|z|)
## (Intercept) < 2e-16
## `myCategory.fctr#Multimedia#` 0.999595
## `myCategory.fctr#Opinion#Room For Debate` 0.988987
## `myCategory.fctr#Opinion#The Public Editor` 0.999992
## `myCategory.fctr#U.S.#Education` 0.999916
## `myCategory.fctrBusiness#Business Day#Dealbook` 0.009016
## `myCategory.fctrBusiness#Business Day#Small Business` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#` 0.999961
## `myCategory.fctrBusiness#Technology#` 0.649773
## `myCategory.fctrCulture#Arts#` 0.083745
## `myCategory.fctrForeign#World#` 0.999895
## `myCategory.fctrForeign#World#Asia Pacific` 0.313136
## `myCategory.fctrMetro#N.Y. / Region#` 0.000182
## myCategory.fctrmyOther 0.999897
## `myCategory.fctrOpEd#Opinion#` 1.91e-15
## `myCategory.fctrScience#Health#` 0.541083
## `myCategory.fctrStyles##Fashion` 0.999869
## `myCategory.fctrStyles#U.S.#` 0.000712
## `myCategory.fctrTravel#Travel#` 0.999770
## `myCategory.fctrTStyle##` 0.537937
## WordCount.log.nonNA 3.29e-15
## H.npnct19.log 2.45e-05
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 0.693860
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 0.988985
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 0.999971
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 0.999557
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 0.021074
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 0.447690
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 0.612217
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 0.032272
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 0.999680
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 0.204933
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 0.000360
## `myCategory.fctrmyOther:WordCount.log.nonNA` 0.999809
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.54e-10
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 0.525960
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 0.999815
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 0.008252
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 0.992511
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 0.795674
## `myCategory.fctr##:.clusterid.fctr2` 0.000516
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.503851
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.596021
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.784419
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.026327
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.629714
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.266248
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 0.766896
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 0.981006
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.564527
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 0.996994
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.011411
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.667289
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 0.604133
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 0.333927
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 0.842490
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 0.304722
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 0.230415
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 0.257316
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 0.288856
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 0.970954
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 0.386057
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 0.113818
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 0.805329
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 0.548224
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 0.024730
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 0.535546
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 0.951630
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 0.997908
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 0.997072
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 0.330234
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 0.079087
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.380927
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 0.785083
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 0.332370
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 0.729228
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 0.223902
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 0.752562
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 0.997397
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 0.063819
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` 0.994887
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.864938
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 0.997833
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 0.030004
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.217351
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 0.997338
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 0.773719
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 0.997050
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 0.871308
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 0.408473
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 0.902661
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 0.292265
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 0.134704
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 0.983699
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 0.998102
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 0.155777
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.997133
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 0.998347
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 0.621965
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.808700
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 0.998033
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 0.677198
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 0.798657
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 0.262505
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 0.171934
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 0.999707
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 0.999209
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 0.999842
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 0.490404
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 0.472297
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 0.934983
## `myCategory.fctrForeign#World#:H.npnct19.log` 0.999934
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 0.999753
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 0.683780
## `myCategory.fctrmyOther:H.npnct19.log` 0.999943
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 0.131099
## `myCategory.fctrScience#Health#:H.npnct19.log` 0.226250
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 0.999910
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 0.576325
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 0.999642
## `myCategory.fctrTStyle##:H.npnct19.log` 0.999479
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 0.352714
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 0.999908
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 0.995805
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 0.999967
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 0.033646
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 0.999961
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 0.781102
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 0.113068
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 0.268322
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 0.740008
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.000000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 0.183400
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 0.110450
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 1.000000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 0.961228
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 0.998602
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 0.972279
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 0.396818
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 0.999744
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 0.994122
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 0.999993
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 0.103518
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 0.999961
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 0.350649
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 0.533928
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 0.347565
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 0.213732
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 1.000000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 0.086991
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 0.044552
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 1.000000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 0.325533
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 0.998603
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 0.716946
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 0.297264
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 0.999965
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 0.994720
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 0.999967
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 0.320046
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 0.999961
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 0.438371
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 0.503836
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 0.222938
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 0.660127
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.000000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 0.074351
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 0.195578
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 1.000000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 0.880800
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 0.998587
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 0.981999
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 0.299547
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 0.999664
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 0.995149
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 0.999994
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 0.297122
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 0.999961
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 0.182415
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 0.512823
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 0.147679
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 0.401693
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.000000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 0.089043
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 0.091037
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 1.000000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 0.091289
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 0.998555
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 0.685378
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 0.351028
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 0.999942
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 0.992683
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 0.999962
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 0.332691
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 0.999961
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 0.520414
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 0.751794
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 0.183651
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 0.920903
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 1.000000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 0.153450
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 0.452040
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 1.000000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 0.820235
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 0.998577
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 0.972913
## `myCategory.fctr##:PubDate.wkend` 0.827141
## `myCategory.fctr#Multimedia#:PubDate.wkend` 0.998698
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 0.999330
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` NA
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 0.500067
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 0.614396
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 0.593198
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 1.56e-05
## `myCategory.fctrForeign#World#:PubDate.wkend` 1.000000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 0.997740
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 0.394635
## `myCategory.fctrmyOther:PubDate.wkend` 1.000000
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 0.078723
## `myCategory.fctrScience#Health#:PubDate.wkend` 0.848895
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 1.000000
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 0.996895
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 0.998226
## `myCategory.fctrTStyle##:PubDate.wkend` 0.995776
## `myCategory.fctr##:H.npnct28.log` 0.411175
## `myCategory.fctr#Multimedia#:H.npnct28.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` NA
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` NA
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 1.000000
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` NA
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` NA
## `myCategory.fctrCulture#Arts#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 0.999029
## `myCategory.fctrmyOther:H.npnct28.log` NA
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` NA
## `myCategory.fctrScience#Health#:H.npnct28.log` NA
## `myCategory.fctrStyles##Fashion:H.npnct28.log` NA
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` NA
## `myCategory.fctrTravel#Travel#:H.npnct28.log` NA
## `myCategory.fctrTStyle##:H.npnct28.log` NA
##
## (Intercept) ***
## `myCategory.fctr#Multimedia#`
## `myCategory.fctr#Opinion#Room For Debate`
## `myCategory.fctr#Opinion#The Public Editor`
## `myCategory.fctr#U.S.#Education`
## `myCategory.fctrBusiness#Business Day#Dealbook` **
## `myCategory.fctrBusiness#Business Day#Small Business` ***
## `myCategory.fctrBusiness#Crosswords/Games#`
## `myCategory.fctrBusiness#Technology#`
## `myCategory.fctrCulture#Arts#` .
## `myCategory.fctrForeign#World#`
## `myCategory.fctrForeign#World#Asia Pacific`
## `myCategory.fctrMetro#N.Y. / Region#` ***
## myCategory.fctrmyOther
## `myCategory.fctrOpEd#Opinion#` ***
## `myCategory.fctrScience#Health#`
## `myCategory.fctrStyles##Fashion`
## `myCategory.fctrStyles#U.S.#` ***
## `myCategory.fctrTravel#Travel#`
## `myCategory.fctrTStyle##`
## WordCount.log.nonNA ***
## H.npnct19.log ***
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA`
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA`
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA`
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA`
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` *
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` ***
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA`
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA`
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` *
## `myCategory.fctrForeign#World#:WordCount.log.nonNA`
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA`
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` ***
## `myCategory.fctrmyOther:WordCount.log.nonNA`
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` ***
## `myCategory.fctrScience#Health#:WordCount.log.nonNA`
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA`
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` **
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA`
## `myCategory.fctrTStyle##:WordCount.log.nonNA`
## `myCategory.fctr##:.clusterid.fctr2` ***
## `myCategory.fctr#Multimedia#:.clusterid.fctr2`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2`
## `myCategory.fctrForeign#World#:.clusterid.fctr2`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2`
## `myCategory.fctrmyOther:.clusterid.fctr2`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` *
## `myCategory.fctrScience#Health#:.clusterid.fctr2`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2`
## `myCategory.fctrTStyle##:.clusterid.fctr2`
## `myCategory.fctr##:.clusterid.fctr3`
## `myCategory.fctr#Multimedia#:.clusterid.fctr3`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3`
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## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` *
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` ***
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## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1`
## `myCategory.fctr##:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` *
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2`
## `myCategory.fctr##:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3`
## `myCategory.fctr##:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4`
## `myCategory.fctr##:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5`
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5`
## `myCategory.fctr##:PubDate.wkend`
## `myCategory.fctr#Multimedia#:PubDate.wkend`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend`
## `myCategory.fctr#U.S.#Education:PubDate.wkend`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend`
## `myCategory.fctrBusiness#Technology#:PubDate.wkend`
## `myCategory.fctrCulture#Arts#:PubDate.wkend` ***
## `myCategory.fctrForeign#World#:PubDate.wkend`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend`
## `myCategory.fctrmyOther:PubDate.wkend`
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` .
## `myCategory.fctrScience#Health#:PubDate.wkend`
## `myCategory.fctrStyles##Fashion:PubDate.wkend`
## `myCategory.fctrStyles#U.S.#:PubDate.wkend`
## `myCategory.fctrTravel#Travel#:PubDate.wkend`
## `myCategory.fctrTStyle##:PubDate.wkend`
## `myCategory.fctr##:H.npnct28.log`
## `myCategory.fctr#Multimedia#:H.npnct28.log`
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log`
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log`
## `myCategory.fctr#U.S.#Education:H.npnct28.log`
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log`
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log`
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log`
## `myCategory.fctrBusiness#Technology#:H.npnct28.log`
## `myCategory.fctrCulture#Arts#:H.npnct28.log`
## `myCategory.fctrForeign#World#:H.npnct28.log`
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log`
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log`
## `myCategory.fctrmyOther:H.npnct28.log`
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log`
## `myCategory.fctrScience#Health#:H.npnct28.log`
## `myCategory.fctrStyles##Fashion:H.npnct28.log`
## `myCategory.fctrStyles#U.S.#:H.npnct28.log`
## `myCategory.fctrTravel#Travel#:H.npnct28.log`
## `myCategory.fctrTStyle##:H.npnct28.log`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1854.3 on 4230 degrees of freedom
## AIC: 2344.3
##
## Number of Fisher Scoring iterations: 20## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleadingpredct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
#stop(here"); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` 1.000000e+02
## `myCategory.fctrBusiness#Business Day#Small Business` 8.241976e+01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 8.115510e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 6.403830e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 6.062123e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 4.594471e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 3.775421e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 3.257174e+01
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 1.064489e+01
## `myCategory.fctrOpEd#Opinion#` 2.948108e-05
## WordCount.log.nonNA 2.922929e-05
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.346224e-05
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 1.602796e-05
## H.npnct19.log 1.565273e-05
## `myCategory.fctrMetro#N.Y. / Region#` 1.388520e-05
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 1.323422e-05
## `myCategory.fctr##:.clusterid.fctr2` 1.287989e-05
## `myCategory.fctrStyles#U.S.#` 1.255605e-05
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 9.799118e-06
## `myCategory.fctrBusiness#Business Day#Dealbook` 9.687334e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 9.384577e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 8.556672e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 8.330148e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 8.240205e-06
## `myCategory.fctr##:.clusterid.fctr10` 8.049864e-06
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 7.942242e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 7.880174e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 7.451966e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 6.875443e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 6.619490e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 6.521922e-06
## `myCategory.fctr##:.clusterid.fctr7` 6.514005e-06
## `myCategory.fctrCulture#Arts#` 6.415142e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 6.348849e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 6.307971e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 6.268971e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 6.264082e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 6.039266e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 5.921093e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 5.877979e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 5.865767e-06
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 5.600610e-06
## `myCategory.fctr##:.clusterid.fctr13` 5.548774e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 5.370610e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 5.295195e-06
## `myCategory.fctr##:.clusterid.fctr14` 5.265397e-06
## `myCategory.fctr##:.clusterid.fctr17` 5.067304e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 4.946152e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 4.935036e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 4.932204e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 4.801099e-06
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 4.702303e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 4.612341e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 4.576115e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 4.521026e-06
## `myCategory.fctr##:.clusterid.fctr8` 4.511615e-06
## `myCategory.fctrScience#Health#:H.npnct19.log` 4.488835e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 4.448832e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.202017e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 4.156516e-06
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 4.124067e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 4.106232e-06
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 3.934480e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 3.906768e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 3.867666e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 3.866536e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 3.848303e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 3.807336e-06
## `myCategory.fctrForeign#World#Asia Pacific` 3.741685e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 3.688642e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 3.647051e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 3.611779e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 3.595855e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 3.593473e-06
## `myCategory.fctr##:.clusterid.fctr4` 3.584297e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 3.484461e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 3.462239e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 3.459515e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 3.447424e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 3.250267e-06
## `myCategory.fctr##:.clusterid.fctr5` 3.215400e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 3.157723e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 3.143170e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.110832e-06
## `myCategory.fctr##:.clusterid.fctr12` 3.066250e-06
## `myCategory.fctr##:H.npnct28.log` 3.048611e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 2.874705e-06
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 2.816553e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 2.789651e-06
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 2.666228e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 2.558353e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 2.501662e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 2.479709e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 2.479624e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.427709e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 2.384154e-06
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 2.352542e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 2.307417e-06
## `myCategory.fctr##:.clusterid.fctr6` 2.298298e-06
## `myCategory.fctrTStyle##` 2.284845e-06
## `myCategory.fctrScience#Health#` 2.267185e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 2.227300e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 2.137184e-06
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 2.072750e-06
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 1.981669e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.966548e-06
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 1.923278e-06
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 1.880414e-06
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 1.868903e-06
## `myCategory.fctr##:.clusterid.fctr15` 1.829058e-06
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 1.788485e-06
## `myCategory.fctrBusiness#Technology#` 1.684421e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 1.631228e-06
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.594630e-06
## `myCategory.fctr##:.clusterid.fctr16` 1.544245e-06
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 1.510938e-06
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 1.502867e-06
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 1.460159e-06
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 1.344872e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.284071e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 1.230986e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.173238e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.169486e-06
## `myCategory.fctr##:.clusterid.fctr3` 1.099648e-06
## `myCategory.fctr##:.clusterid.fctr11` 1.066546e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.030820e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 1.014805e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 1.011597e-06
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 9.605892e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 9.462511e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 9.142420e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 8.980943e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 8.429663e-07
## `myCategory.fctr##:PubDate.wkend` 8.100515e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 7.371228e-07
## `myCategory.fctrScience#Health#:PubDate.wkend` 7.067731e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 6.309615e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 6.009388e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 5.562643e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 4.536787e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.683459e-07
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 3.026130e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 2.250212e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 1.803294e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 1.350735e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.289087e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.259583e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 8.831372e-08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 8.370014e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 7.579186e-08
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 5.121507e-08
## `myCategory.fctr#Opinion#Room For Debate` 5.120522e-08
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 3.481721e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 3.401713e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 2.733009e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 2.454686e-08
## `myCategory.fctr##:.clusterid.fctr9` 2.376979e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 2.255188e-08
## `myCategory.fctrTStyle##:PubDate.wkend` 1.963644e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 1.950169e-08
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 1.443475e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 1.397598e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.371392e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.361231e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 1.332884e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 1.237475e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 1.210140e-08
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 1.050683e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 1.007279e-08
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 9.727372e-09
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 9.146561e-09
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 8.825715e-09
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 8.248431e-09
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 7.684549e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 6.719480e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 6.617133e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 6.569980e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 6.501329e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 6.496246e-09
## `myCategory.fctr#Multimedia#:PubDate.wkend` 6.054326e-09
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 4.515547e-09
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 3.679566e-09
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 3.115215e-09
## `myCategory.fctrTStyle##:H.npnct19.log` 2.423739e-09
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 2.060667e-09
## `myCategory.fctr#Multimedia#` 1.884324e-09
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 1.664927e-09
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 1.561113e-09
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 1.487501e-09
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 1.360716e-09
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 1.191237e-09
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 1.148850e-09
## `myCategory.fctrTravel#Travel#` 1.068490e-09
## `myCategory.fctrmyOther:WordCount.log.nonNA` 8.882188e-10
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 8.614427e-10
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 7.363973e-10
## `myCategory.fctrStyles##Fashion` 6.095459e-10
## `myCategory.fctrForeign#World#` 4.863020e-10
## myCategory.fctrmyOther 4.775128e-10
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 4.271925e-10
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 4.200071e-10
## `myCategory.fctr#U.S.#Education` 3.892163e-10
## `myCategory.fctrForeign#World#:H.npnct19.log` 3.078433e-10
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 2.706821e-10
## `myCategory.fctrmyOther:H.npnct19.log` 2.644820e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 1.818306e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 1.814126e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 1.813739e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 1.810528e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 1.803460e-10
## `myCategory.fctrBusiness#Crosswords/Games#` 1.800654e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 1.756708e-10
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 1.604906e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 1.520273e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 1.517043e-10
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 1.346338e-10
## `myCategory.fctr#Opinion#The Public Editor` 3.912648e-11
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 3.084007e-11
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 2.601071e-11
## `myCategory.fctrForeign#World#:PubDate.wkend` 6.774083e-14
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 4.770371e-14
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 4.121083e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 3.179932e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 3.147951e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 3.122164e-14
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 2.305464e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 1.404121e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 9.838308e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 9.218443e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 9.013747e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 8.725064e-15
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 8.244360e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 7.798167e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 6.286373e-15
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 6.182610e-15
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 5.359319e-15
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 3.815601e-15
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 3.478377e-15
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 1.839848e-15
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 1.831037e-15
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 8.519852e-16
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.598518e-16
## `myCategory.fctrmyOther:PubDate.wkend` 1.169430e-16
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 0.000000e+00
## Rank9.glm.importance
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` 1.000000e+02
## `myCategory.fctrBusiness#Business Day#Small Business` 8.241976e+01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 8.115510e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 6.403830e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 6.062123e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 4.594471e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 3.775421e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 3.257174e+01
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 1.064489e+01
## `myCategory.fctrOpEd#Opinion#` 2.948108e-05
## WordCount.log.nonNA 2.922929e-05
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.346224e-05
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 1.602796e-05
## H.npnct19.log 1.565273e-05
## `myCategory.fctrMetro#N.Y. / Region#` 1.388520e-05
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 1.323422e-05
## `myCategory.fctr##:.clusterid.fctr2` 1.287989e-05
## `myCategory.fctrStyles#U.S.#` 1.255605e-05
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 9.799118e-06
## `myCategory.fctrBusiness#Business Day#Dealbook` 9.687334e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 9.384577e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 8.556672e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 8.330148e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 8.240205e-06
## `myCategory.fctr##:.clusterid.fctr10` 8.049864e-06
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 7.942242e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 7.880174e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 7.451966e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 6.875443e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 6.619490e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 6.521922e-06
## `myCategory.fctr##:.clusterid.fctr7` 6.514005e-06
## `myCategory.fctrCulture#Arts#` 6.415142e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 6.348849e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 6.307971e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 6.268971e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 6.264082e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 6.039266e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 5.921093e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 5.877979e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 5.865767e-06
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 5.600610e-06
## `myCategory.fctr##:.clusterid.fctr13` 5.548774e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 5.370610e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 5.295195e-06
## `myCategory.fctr##:.clusterid.fctr14` 5.265397e-06
## `myCategory.fctr##:.clusterid.fctr17` 5.067304e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 4.946152e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 4.935036e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 4.932204e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 4.801099e-06
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 4.702303e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 4.612341e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 4.576115e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 4.521026e-06
## `myCategory.fctr##:.clusterid.fctr8` 4.511615e-06
## `myCategory.fctrScience#Health#:H.npnct19.log` 4.488835e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 4.448832e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.202017e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 4.156516e-06
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 4.124067e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 4.106232e-06
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 3.934480e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 3.906768e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 3.867666e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 3.866536e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 3.848303e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 3.807336e-06
## `myCategory.fctrForeign#World#Asia Pacific` 3.741685e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 3.688642e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 3.647051e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 3.611779e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 3.595855e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 3.593473e-06
## `myCategory.fctr##:.clusterid.fctr4` 3.584297e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 3.484461e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 3.462239e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 3.459515e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 3.447424e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 3.250267e-06
## `myCategory.fctr##:.clusterid.fctr5` 3.215400e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 3.157723e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 3.143170e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.110832e-06
## `myCategory.fctr##:.clusterid.fctr12` 3.066250e-06
## `myCategory.fctr##:H.npnct28.log` 3.048611e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 2.874705e-06
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 2.816553e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 2.789651e-06
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 2.666228e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 2.558353e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 2.501662e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 2.479709e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 2.479624e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.427709e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 2.384154e-06
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 2.352542e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 2.307417e-06
## `myCategory.fctr##:.clusterid.fctr6` 2.298298e-06
## `myCategory.fctrTStyle##` 2.284845e-06
## `myCategory.fctrScience#Health#` 2.267185e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 2.227300e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 2.137184e-06
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 2.072750e-06
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 1.981669e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.966548e-06
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 1.923278e-06
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 1.880414e-06
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 1.868903e-06
## `myCategory.fctr##:.clusterid.fctr15` 1.829058e-06
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 1.788485e-06
## `myCategory.fctrBusiness#Technology#` 1.684421e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 1.631228e-06
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.594630e-06
## `myCategory.fctr##:.clusterid.fctr16` 1.544245e-06
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 1.510938e-06
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 1.502867e-06
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 1.460159e-06
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 1.344872e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.284071e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 1.230986e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.173238e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.169486e-06
## `myCategory.fctr##:.clusterid.fctr3` 1.099648e-06
## `myCategory.fctr##:.clusterid.fctr11` 1.066546e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.030820e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 1.014805e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 1.011597e-06
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 9.605892e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 9.462511e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 9.142420e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 8.980943e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 8.429663e-07
## `myCategory.fctr##:PubDate.wkend` 8.100515e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 7.371228e-07
## `myCategory.fctrScience#Health#:PubDate.wkend` 7.067731e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 6.309615e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 6.009388e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 5.562643e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 4.536787e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.683459e-07
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 3.026130e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 2.250212e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 1.803294e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 1.350735e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.289087e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.259583e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 8.831372e-08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 8.370014e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 7.579186e-08
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 5.121507e-08
## `myCategory.fctr#Opinion#Room For Debate` 5.120522e-08
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 3.481721e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 3.401713e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 2.733009e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 2.454686e-08
## `myCategory.fctr##:.clusterid.fctr9` 2.376979e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 2.255188e-08
## `myCategory.fctrTStyle##:PubDate.wkend` 1.963644e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 1.950169e-08
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 1.443475e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 1.397598e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.371392e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.361231e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 1.332884e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 1.237475e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 1.210140e-08
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 1.050683e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 1.007279e-08
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 9.727372e-09
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 9.146561e-09
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 8.825715e-09
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 8.248431e-09
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 7.684549e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 6.719480e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 6.617133e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 6.569980e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 6.501329e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 6.496246e-09
## `myCategory.fctr#Multimedia#:PubDate.wkend` 6.054326e-09
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 4.515547e-09
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 3.679566e-09
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 3.115215e-09
## `myCategory.fctrTStyle##:H.npnct19.log` 2.423739e-09
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 2.060667e-09
## `myCategory.fctr#Multimedia#` 1.884324e-09
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 1.664927e-09
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 1.561113e-09
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 1.487501e-09
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 1.360716e-09
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 1.191237e-09
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 1.148850e-09
## `myCategory.fctrTravel#Travel#` 1.068490e-09
## `myCategory.fctrmyOther:WordCount.log.nonNA` 8.882188e-10
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 8.614427e-10
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 7.363973e-10
## `myCategory.fctrStyles##Fashion` 6.095459e-10
## `myCategory.fctrForeign#World#` 4.863020e-10
## myCategory.fctrmyOther 4.775128e-10
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 4.271925e-10
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 4.200071e-10
## `myCategory.fctr#U.S.#Education` 3.892163e-10
## `myCategory.fctrForeign#World#:H.npnct19.log` 3.078433e-10
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 2.706821e-10
## `myCategory.fctrmyOther:H.npnct19.log` 2.644820e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 1.818306e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 1.814126e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 1.813739e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 1.810528e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 1.803460e-10
## `myCategory.fctrBusiness#Crosswords/Games#` 1.800654e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 1.756708e-10
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 1.604906e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 1.520273e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 1.517043e-10
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 1.346338e-10
## `myCategory.fctr#Opinion#The Public Editor` 3.912648e-11
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 3.084007e-11
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 2.601071e-11
## `myCategory.fctrForeign#World#:PubDate.wkend` 6.774083e-14
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 4.770371e-14
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 4.121083e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 3.179932e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 3.147951e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 3.122164e-14
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 2.305464e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 1.404121e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 9.838308e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 9.218443e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 9.013747e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 8.725064e-15
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 8.244360e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 7.798167e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 6.286373e-15
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 6.182610e-15
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 5.359319e-15
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 3.815601e-15
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 3.478377e-15
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 1.839848e-15
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 1.831037e-15
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 8.519852e-16
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.598518e-16
## `myCategory.fctrmyOther:PubDate.wkend` 1.169430e-16
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 0.000000e+00# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_vars)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_vars,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id, : Limiting important feature scatter plots to 5 out of 13
## [1] "Min/Max Boundaries: "
## [1] UniqueID
## [2] Popular.fctr
## [3] Popular.fctr.predict.Rank9.glm.prob
## [4] Popular.fctr.predict.Rank9.glm
## [5] Popular.fctr.predict.Rank9.glm.accurate
## [6] Popular.fctr.predict.Rank9.glm.error
## [7] .label
## <0 rows> (or 0-length row.names)
## [1] "Inaccurate: "
## UniqueID Popular.fctr Popular.fctr.predict.Rank9.glm.prob
## 2538 2538 Y 2.220446e-16
## 5395 5395 Y 2.220446e-16
## 172 172 Y 4.087101e-10
## 5486 5486 Y 4.305008e-10
## 1928 1928 Y 2.155079e-09
## 4 4 Y 5.267855e-09
## Popular.fctr.predict.Rank9.glm
## 2538 N
## 5395 N
## 172 N
## 5486 N
## 1928 N
## 4 N
## Popular.fctr.predict.Rank9.glm.accurate
## 2538 FALSE
## 5395 FALSE
## 172 FALSE
## 5486 FALSE
## 1928 FALSE
## 4 FALSE
## Popular.fctr.predict.Rank9.glm.error
## 2538 -0.5
## 5395 -0.5
## 172 -0.5
## 5486 -0.5
## 1928 -0.5
## 4 -0.5
## UniqueID Popular.fctr Popular.fctr.predict.Rank9.glm.prob
## 3882 3882 Y 0.2178832
## 3357 3357 Y 0.2244332
## 3366 3366 Y 0.2401678
## 3263 3263 Y 0.2709487
## 1685 1685 N 0.6800874
## 664 664 N 0.8215675
## Popular.fctr.predict.Rank9.glm
## 3882 N
## 3357 N
## 3366 N
## 3263 N
## 1685 Y
## 664 Y
## Popular.fctr.predict.Rank9.glm.accurate
## 3882 FALSE
## 3357 FALSE
## 3366 FALSE
## 3263 FALSE
## 1685 FALSE
## 664 FALSE
## Popular.fctr.predict.Rank9.glm.error
## 3882 -0.2821168
## 3357 -0.2755668
## 3366 -0.2598322
## 3263 -0.2290513
## 1685 0.1800874
## 664 0.3215675
## UniqueID Popular.fctr Popular.fctr.predict.Rank9.glm.prob
## 5523 5523 N 0.9999999
## 3875 3875 N 1.0000000
## 4377 4377 N 1.0000000
## 5215 5215 N 1.0000000
## 6246 6246 N 1.0000000
## 1639 1639 N 1.0000000
## Popular.fctr.predict.Rank9.glm
## 5523 Y
## 3875 Y
## 4377 Y
## 5215 Y
## 6246 Y
## 1639 Y
## Popular.fctr.predict.Rank9.glm.accurate
## 5523 FALSE
## 3875 FALSE
## 4377 FALSE
## 5215 FALSE
## 6246 FALSE
## 1639 FALSE
## Popular.fctr.predict.Rank9.glm.error
## 5523 0.4999999
## 3875 0.5000000
## 4377 0.5000000
## 5215 0.5000000
## 6246 0.5000000
## 1639 0.5000000
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
FN_OOB_ids <- c(4721, 4020, 693, 92)
print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])## [1] Popular.fctr
## [2] Popular.fctr.predict.Rank9.glm.prob
## [3] Popular.fctr.predict.Rank9.glm
## [4] Popular.fctr.predict.Rank9.glm.accurate
## <0 rows> (or 0-length row.names)print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
glb_feats_df$id[1:5]])## [1] Popular WordCount.log A.ratio.sum.TfIdf.nwrds
## [4] WordCount.log.nonNA S.ratio.sum.TfIdf.nwrds
## <0 rows> (or 0-length row.names)print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
glb_txt_vars])## [1] Headline Snippet Abstract
## <0 rows> (or 0-length row.names)write.csv(glb_OOBobs_df[, c(glb_id_vars,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 1066.479 1090.815 24.337
## 13 fit.models 7 3 1090.816 NA NAprint(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "PubDate.year.fctr" "H.npnct03.log" "H.npnct09.log"
## [4] "H.npnct10.log" "H.npnct17.log" "H.npnct18.log"
## [7] "H.npnct20.log" "H.npnct21.log" "H.npnct22.log"
## [10] "H.npnct23.log" "H.npnct24.log" "H.npnct25.log"
## [13] "H.npnct26.log" "H.npnct27.log" "H.npnct29.log"
## [16] "H.npnct30.log" "H.P.http" "S.npnct02.log"
## [19] "S.npnct05.log" "S.npnct07.log" "S.npnct09.log"
## [22] "S.npnct10.log" "S.npnct17.log" "S.npnct18.log"
## [25] "S.npnct21.log" "S.npnct22.log" "S.npnct23.log"
## [28] "S.npnct25.log" "S.npnct26.log" "S.npnct27.log"
## [31] "S.npnct29.log" "S.npnct30.log" "S.P.http"
## [34] "A.npnct05.log" "A.npnct07.log" "A.npnct09.log"
## [37] "A.npnct10.log" "A.npnct21.log" "A.npnct22.log"
## [40] "A.npnct23.log" "A.npnct25.log" "A.npnct26.log"
## [43] "A.npnct27.log" "A.npnct29.log" "A.npnct30.log"print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## [1] "PubDate.year.fctr" "H.npnct03.log" "H.npnct09.log"
## [4] "H.npnct10.log" "H.npnct17.log" "H.npnct18.log"
## [7] "H.npnct20.log" "H.npnct21.log" "H.npnct22.log"
## [10] "H.npnct23.log" "H.npnct24.log" "H.npnct25.log"
## [13] "H.npnct26.log" "H.npnct27.log" "H.npnct29.log"
## [16] "H.npnct30.log" "H.P.http" "S.npnct02.log"
## [19] "S.npnct05.log" "S.npnct07.log" "S.npnct09.log"
## [22] "S.npnct10.log" "S.npnct17.log" "S.npnct18.log"
## [25] "S.npnct21.log" "S.npnct22.log" "S.npnct23.log"
## [28] "S.npnct25.log" "S.npnct26.log" "S.npnct27.log"
## [31] "S.npnct29.log" "S.npnct30.log" "S.P.http"
## [34] "A.npnct05.log" "A.npnct07.log" "A.npnct09.log"
## [37] "A.npnct10.log" "A.npnct21.log" "A.npnct22.log"
## [40] "A.npnct23.log" "A.npnct25.log" "A.npnct26.log"
## [43] "A.npnct27.log" "A.npnct29.log" "A.npnct30.log"print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "PubDate.year.fctr"
## [2] "H.npnct03.log"
## [3] "H.npnct09.log"
## [4] "H.npnct10.log"
## [5] "H.npnct17.log"
## [6] "H.npnct18.log"
## [7] "H.npnct20.log"
## [8] "H.npnct21.log"
## [9] "H.npnct22.log"
## [10] "H.npnct23.log"
## [11] "H.npnct24.log"
## [12] "H.npnct25.log"
## [13] "H.npnct26.log"
## [14] "H.npnct27.log"
## [15] "H.npnct29.log"
## [16] "H.npnct30.log"
## [17] "H.P.http"
## [18] "S.npnct02.log"
## [19] "S.npnct05.log"
## [20] "S.npnct07.log"
## [21] "S.npnct09.log"
## [22] "S.npnct10.log"
## [23] "S.npnct17.log"
## [24] "S.npnct18.log"
## [25] "S.npnct21.log"
## [26] "S.npnct22.log"
## [27] "S.npnct23.log"
## [28] "S.npnct25.log"
## [29] "S.npnct26.log"
## [30] "S.npnct27.log"
## [31] "S.npnct29.log"
## [32] "S.npnct30.log"
## [33] "S.P.http"
## [34] "A.npnct05.log"
## [35] "A.npnct07.log"
## [36] "A.npnct09.log"
## [37] "A.npnct10.log"
## [38] "A.npnct21.log"
## [39] "A.npnct22.log"
## [40] "A.npnct23.log"
## [41] "A.npnct25.log"
## [42] "A.npnct26.log"
## [43] "A.npnct27.log"
## [44] "A.npnct29.log"
## [45] "A.npnct30.log"
## [46] "Popular.fctr.predict.Rank9.glm.prob"
## [47] "Popular.fctr.predict.Rank9.glm"
## [48] "Popular.fctr.predict.Rank9.glm.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 1090.816 1097.571 6.755
## 14 fit.data.training 8 0 1097.571 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size## [1] "fitting model: Final.glm"
## [1] " indep_vars: myCategory.fctr*WordCount.log.nonNA, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5, myCategory.fctr:PubDate.wkend, myCategory.fctr:H.npnct28.log"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 3218, 3261, 3693
## Warning: not plotting observations with leverage one:
## 3218, 3261, 3693
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.4904 -0.2423 -0.0002 0.0000 8.4904
##
## Coefficients: (273 not defined because of singularities)
## Estimate
## (Intercept) -1.333e+01
## `myCategory.fctr#Multimedia#` -1.566e+01
## `myCategory.fctr#Opinion#Room For Debate` 2.870e+16
## `myCategory.fctr#Opinion#The Public Editor` 2.102e+05
## `myCategory.fctr#U.S.#Education` 4.808e+00
## `myCategory.fctrBusiness#Business Day#Dealbook` -1.004e+01
## `myCategory.fctrBusiness#Business Day#Small Business` -1.871e+16
## `myCategory.fctrBusiness#Crosswords/Games#` 3.455e+04
## `myCategory.fctrBusiness#Technology#` -7.987e-01
## `myCategory.fctrCulture#Arts#` 7.301e+00
## `myCategory.fctrForeign#World#` -1.042e+01
## `myCategory.fctrForeign#World#Asia Pacific` -1.912e+01
## `myCategory.fctrMetro#N.Y. / Region#` 8.345e+00
## myCategory.fctrmyOther -2.762e+15
## `myCategory.fctrOpEd#Opinion#` 1.294e+01
## `myCategory.fctrScience#Health#` 3.578e-01
## `myCategory.fctrStyles##Fashion` -2.742e+15
## `myCategory.fctrStyles#U.S.#` 1.058e+01
## `myCategory.fctrTravel#Travel#` 1.108e+03
## `myCategory.fctrTStyle##` -4.425e+15
## WordCount.log.nonNA 1.759e+00
## H.npnct19.log 1.624e+00
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` -7.649e-01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` -4.530e+15
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 5.057e+01
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` -1.595e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 1.339e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 1.771e+14
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` -4.192e-01
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 1.490e-01
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` -1.364e+00
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` -2.441e+00
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` -2.259e+00
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` -1.257e+00
## `myCategory.fctrmyOther:WordCount.log.nonNA` -3.289e+14
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` -1.450e+00
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 5.495e-01
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` -2.690e+14
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` -1.333e+00
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 8.361e+01
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 4.407e+14
## `myCategory.fctr##:.clusterid.fctr2` -2.844e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` -4.782e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.633e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 4.247e-02
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -6.984e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -7.030e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -3.675e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -1.643e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 1.893e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 4.891e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -2.252e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` -6.002e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -2.481e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -1.855e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` -1.477e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 1.149e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 6.632e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.275e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` -4.871e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` -5.553e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 1.101e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` -9.164e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 8.984e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -2.765e-02
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 5.259e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` -6.332e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` -1.367e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -7.077e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -2.308e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -2.242e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` -3.425e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` -9.798e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 3.375e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 7.023e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 7.036e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 4.015e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 3.887e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` -2.481e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -2.319e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` -1.304e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -2.458e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 6.698e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -2.399e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` -1.826e+18
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` -9.799e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 9.721e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 6.040e+00
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 2.589e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -8.472e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -2.752e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` -2.202e+16
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 3.008e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.562e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` -5.926e+16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -1.598e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` -1.079e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 4.790e+16
## `myCategory.fctr##:PubDate.wkend` 2.067e-01
## `myCategory.fctr#Multimedia#:PubDate.wkend` -2.278e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 1.136e+14
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` 5.996e+01
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 1.333e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` -2.000e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` -1.108e+15
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` -1.052e+00
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` -3.848e-01
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 2.300e+00
## `myCategory.fctrForeign#World#:PubDate.wkend` 2.116e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` -1.855e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` -1.034e-01
## `myCategory.fctrmyOther:PubDate.wkend` -1.072e+15
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 8.662e-01
## `myCategory.fctrScience#Health#:PubDate.wkend` 4.756e-01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 2.276e+15
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 4.823e-01
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 2.918e+00
## `myCategory.fctrTStyle##:PubDate.wkend` -1.068e+15
## `myCategory.fctr##:H.npnct28.log` 5.990e-01
## `myCategory.fctr#Multimedia#:H.npnct28.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` NA
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` NA
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 3.593e-01
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` NA
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` NA
## `myCategory.fctrCulture#Arts#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` -3.656e+01
## `myCategory.fctrmyOther:H.npnct28.log` NA
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` NA
## `myCategory.fctrScience#Health#:H.npnct28.log` NA
## `myCategory.fctrStyles##Fashion:H.npnct28.log` NA
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` NA
## `myCategory.fctrTravel#Travel#:H.npnct28.log` NA
## `myCategory.fctrTStyle##:H.npnct28.log` NA
## Std. Error
## (Intercept) 1.310e+00
## `myCategory.fctr#Multimedia#` 2.008e+05
## `myCategory.fctr#Opinion#Room For Debate` 2.567e+08
## `myCategory.fctr#Opinion#The Public Editor` 1.942e+07
## `myCategory.fctr#U.S.#Education` 4.306e+03
## `myCategory.fctrBusiness#Business Day#Dealbook` 2.875e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 2.478e+08
## `myCategory.fctrBusiness#Crosswords/Games#` 3.068e+07
## `myCategory.fctrBusiness#Technology#` 3.155e+00
## `myCategory.fctrCulture#Arts#` 2.501e+00
## `myCategory.fctrForeign#World#` 2.969e+05
## `myCategory.fctrForeign#World#Asia Pacific` 2.804e+01
## `myCategory.fctrMetro#N.Y. / Region#` 2.209e+00
## myCategory.fctrmyOther 1.024e+08
## `myCategory.fctrOpEd#Opinion#` 1.451e+00
## `myCategory.fctrScience#Health#` 2.444e+00
## `myCategory.fctrStyles##Fashion` 9.532e+07
## `myCategory.fctrStyles#U.S.#` 2.132e+00
## `myCategory.fctrTravel#Travel#` 1.025e+06
## `myCategory.fctrTStyle##` 1.706e+07
## WordCount.log.nonNA 1.900e-01
## H.npnct19.log 3.930e-01
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 2.694e+00
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 3.182e+07
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 2.222e+04
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 6.255e+02
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 4.112e-01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 1.941e+07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 7.615e-01
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 4.803e-01
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 4.103e-01
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 5.266e+04
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 1.351e+00
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 3.601e-01
## `myCategory.fctrmyOther:WordCount.log.nonNA` 1.258e+07
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.159e-01
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 3.861e-01
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 1.528e+07
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 3.114e-01
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 5.807e+04
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 2.167e+06
## `myCategory.fctr##:.clusterid.fctr2` 6.614e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 4.770e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 6.018e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 8.112e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 3.471e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 5.612e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 4.098e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 3.894e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 5.180e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 4.965e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 4.077e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 3.411e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 5.699e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 4.666e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 1.071e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 6.311e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 5.391e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 9.170e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.389e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 5.333e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 7.347e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 6.922e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 5.268e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 6.560e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 8.957e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 4.547e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 4.208e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 7.178e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 5.765e+04
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 4.657e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 5.055e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 5.232e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 5.982e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 5.990e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 1.179e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 7.810e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 4.027e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 6.050e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 4.018e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 5.559e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` 3.123e+04
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 1.176e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 6.026e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 1.054e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 5.579e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 5.636e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 5.826e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 8.277e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 9.439e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 1.097e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 7.763e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.199e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 6.421e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 6.749e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 7.380e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 8.067e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 4.436e+04
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 1.282e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 9.048e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 8.366e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 6.946e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 1.276e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 1.191e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 1.173e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 8.688e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 3.118e+07
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 9.137e+05
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.988e+03
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 2.735e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 2.490e+07
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 5.580e+05
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 2.315e+00
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 1.168e+00
## `myCategory.fctrForeign#World#:H.npnct19.log` 3.653e+05
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 3.103e+05
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 1.357e+00
## `myCategory.fctrmyOther:H.npnct19.log` 5.486e+07
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 7.439e-01
## `myCategory.fctrScience#Health#:H.npnct19.log` 9.586e-01
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 3.587e+07
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 8.123e-01
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 2.810e+05
## `myCategory.fctrTStyle##:H.npnct19.log` 5.632e+07
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 7.511e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 6.289e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 9.386e+09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 5.094e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 1.055e+06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 1.593e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 1.591e+11
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 5.987e+09
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.495e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 2.380e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 2.716e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 7.362e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 4.116e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 2.519e+10
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 2.047e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 4.792e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 5.565e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 8.129e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 2.201e+08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 3.164e+09
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 8.434e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 2.979e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 3.502e+09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 4.872e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 9.109e+05
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 1.496e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 7.089e+10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 7.563e+09
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 1.508e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 2.260e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 4.785e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 6.488e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 4.896e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 1.244e+10
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 2.637e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 3.022e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 6.425e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 1.018e+02
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 2.714e+08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 3.730e+09
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.042e+02
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 6.241e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 1.665e+10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 5.277e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 1.251e+06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 1.411e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 1.908e+11
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 6.217e+09
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 1.427e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 2.158e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 3.956e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 8.004e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 4.606e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 3.281e+10
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 3.592e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 2.787e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 6.976e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 1.021e+02
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 2.714e+08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 4.399e+09
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 4.792e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 1.919e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 1.456e+10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 1.753e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 5.371e+05
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.687e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 3.151e+10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 2.927e+09
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 1.517e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.435e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 2.274e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 1.582e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.506e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 7.862e+09
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 2.830e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 3.845e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 7.655e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 5.634e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 1.239e+08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 2.046e+09
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 4.259e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 4.285e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 6.685e+09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 9.791e+08
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 4.303e+05
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 1.530e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 5.280e+10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 8.634e+08
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 1.431e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.873e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 1.368e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 7.936e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.230e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 1.225e+10
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 2.032e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.357e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 6.328e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 5.392e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 5.819e+07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.516e+09
## `myCategory.fctr##:PubDate.wkend` 4.755e-01
## `myCategory.fctr#Multimedia#:PubDate.wkend` 1.342e+05
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 2.250e+07
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` 3.560e+05
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 7.831e+03
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 5.414e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` 4.849e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 1.181e+00
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 5.802e-01
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 4.656e-01
## `myCategory.fctrForeign#World#:PubDate.wkend` 5.001e+04
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 1.842e+04
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 9.461e-01
## `myCategory.fctrmyOther:PubDate.wkend` 2.796e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 4.003e-01
## `myCategory.fctrScience#Health#:PubDate.wkend` 8.008e-01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 2.049e+07
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 6.747e-01
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 6.490e+04
## `myCategory.fctrTStyle##:PubDate.wkend` 7.943e+06
## `myCategory.fctr##:H.npnct28.log` 1.609e+00
## `myCategory.fctr#Multimedia#:H.npnct28.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` NA
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` NA
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 1.741e+03
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` NA
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` NA
## `myCategory.fctrCulture#Arts#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 2.419e+05
## `myCategory.fctrmyOther:H.npnct28.log` NA
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` NA
## `myCategory.fctrScience#Health#:H.npnct28.log` NA
## `myCategory.fctrStyles##Fashion:H.npnct28.log` NA
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` NA
## `myCategory.fctrTravel#Travel#:H.npnct28.log` NA
## `myCategory.fctrTStyle##:H.npnct28.log` NA
## z value
## (Intercept) -1.017e+01
## `myCategory.fctr#Multimedia#` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate` 1.118e+08
## `myCategory.fctr#Opinion#The Public Editor` 1.100e-02
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## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 3.960e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 0.000e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` -4.870e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` -8.630e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 5.770e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` -9.620e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 7.796e+05
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 1.000e-02
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` -8.070e-01
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 2.389e+07
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 0.000e+00
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 2.022e+00
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 1.138e+00
## `myCategory.fctrForeign#World#:H.npnct19.log` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 0.000e+00
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 1.120e+00
## `myCategory.fctrmyOther:H.npnct19.log` 2.644e+07
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` -1.753e+00
## `myCategory.fctrScience#Health#:H.npnct19.log` -1.175e+00
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 5.469e+07
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` -9.800e-02
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 0.000e+00
## `myCategory.fctrTStyle##:H.npnct19.log` -2.251e+07
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 5.530e-01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 2.755e+07
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` -1.100e-02
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 2.000e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 2.182e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` -4.407e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` -1.000e-03
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 4.700e-01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` -2.819e+00
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` -8.720e-01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 1.164e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` -4.102e+06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 3.640e-01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` -2.184e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` -4.263e+07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 2.480e-01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` -2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 4.375e+07
## `myCategory.fctr##:PubDate.day.minutes.poly.2` -1.338e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 2.986e+07
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 1.100e-02
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 2.000e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 9.880e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` -2.332e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 1.000e-03
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 8.000e-03
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 1.328e+00
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 7.950e-01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` -1.315e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` -5.526e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` -1.587e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 2.058e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 8.917e+07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` -9.980e-01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` -7.230e+07
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 4.680e-01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` -1.155e+07
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` -1.100e-02
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 2.000e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` -6.560e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` -3.976e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` -1.000e-03
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` -1.000e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 1.953e+00
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` -1.066e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` -5.350e-01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` -2.854e+06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 1.939e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 1.851e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 1.036e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` -3.250e-01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` -2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 4.769e+07
## `myCategory.fctr##:PubDate.day.minutes.poly.4` -1.540e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 9.431e+06
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 1.100e-02
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 2.000e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.939e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` -2.975e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 1.000e-03
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` -1.014e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` -5.870e-01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` -1.234e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 1.337e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` -4.599e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` -1.631e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` -1.850e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 3.395e+06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` -1.382e+00
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` -8.208e+07
## `myCategory.fctr##:PubDate.day.minutes.poly.5` -9.700e-02
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 2.218e+06
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` -1.100e-02
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 2.000e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -1.568e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` -3.459e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` -1.000e-03
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 6.790e-01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 3.220e-01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -1.068e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -8.520e-01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` -1.797e+06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 1.480e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 1.061e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` -9.364e+07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -2.960e-01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` -2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 3.160e+07
## `myCategory.fctr##:PubDate.wkend` 4.350e-01
## `myCategory.fctr#Multimedia#:PubDate.wkend` 0.000e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 5.047e+06
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` 0.000e+00
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` -3.690e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` -2.285e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` -8.900e-01
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` -6.630e-01
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 4.939e+00
## `myCategory.fctrForeign#World#:PubDate.wkend` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` -1.000e-03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` -1.090e-01
## `myCategory.fctrmyOther:PubDate.wkend` -3.835e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 2.164e+00
## `myCategory.fctrScience#Health#:PubDate.wkend` 5.940e-01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 1.111e+08
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 7.150e-01
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 0.000e+00
## `myCategory.fctrTStyle##:PubDate.wkend` -1.345e+08
## `myCategory.fctr##:H.npnct28.log` 3.720e-01
## `myCategory.fctr#Multimedia#:H.npnct28.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` NA
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` NA
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 0.000e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` NA
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` NA
## `myCategory.fctrCulture#Arts#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 0.000e+00
## `myCategory.fctrmyOther:H.npnct28.log` NA
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` NA
## `myCategory.fctrScience#Health#:H.npnct28.log` NA
## `myCategory.fctrStyles##Fashion:H.npnct28.log` NA
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` NA
## `myCategory.fctrTravel#Travel#:H.npnct28.log` NA
## `myCategory.fctrTStyle##:H.npnct28.log` NA
## Pr(>|z|)
## (Intercept) < 2e-16
## `myCategory.fctr#Multimedia#` 0.999938
## `myCategory.fctr#Opinion#Room For Debate` < 2e-16
## `myCategory.fctr#Opinion#The Public Editor` 0.991365
## `myCategory.fctr#U.S.#Education` 0.999109
## `myCategory.fctrBusiness#Business Day#Dealbook` 0.000479
## `myCategory.fctrBusiness#Business Day#Small Business` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#` 0.999101
## `myCategory.fctrBusiness#Technology#` 0.800162
## `myCategory.fctrCulture#Arts#` 0.003505
## `myCategory.fctrForeign#World#` 0.999972
## `myCategory.fctrForeign#World#Asia Pacific` 0.495285
## `myCategory.fctrMetro#N.Y. / Region#` 0.000159
## myCategory.fctrmyOther < 2e-16
## `myCategory.fctrOpEd#Opinion#` < 2e-16
## `myCategory.fctrScience#Health#` 0.883622
## `myCategory.fctrStyles##Fashion` < 2e-16
## `myCategory.fctrStyles#U.S.#` 7.02e-07
## `myCategory.fctrTravel#Travel#` 0.999138
## `myCategory.fctrTStyle##` < 2e-16
## WordCount.log.nonNA < 2e-16
## H.npnct19.log 3.57e-05
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 0.776513
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` < 2e-16
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 0.998184
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 0.997965
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 0.001131
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 0.582003
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 0.756463
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 0.000883
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 0.999963
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 0.094462
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 0.000482
## `myCategory.fctrmyOther:WordCount.log.nonNA` < 2e-16
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 1.89e-11
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 0.154691
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` < 2e-16
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 1.87e-05
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 0.998851
## `myCategory.fctrTStyle##:WordCount.log.nonNA` < 2e-16
## `myCategory.fctr##:.clusterid.fctr2` 1.71e-05
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.920158
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.786058
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.958243
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.044205
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.210353
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.369818
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 0.673075
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
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## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
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## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
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## `myCategory.fctr##:.clusterid.fctr7` 0.061108
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.572604
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 0.550727
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
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## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
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## `myCategory.fctr##:.clusterid.fctr8` 0.334424
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 0.681774
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 0.999540
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
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## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` 0.999372
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.954581
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 0.999703
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
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## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 0.014667
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
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## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.186249
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 0.999670
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
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## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
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## `myCategory.fctr##:.clusterid.fctr11` 0.721865
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
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## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 0.604478
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 0.555027
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
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## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 0.838019
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 0.628343
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
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## `myCategory.fctr##:.clusterid.fctr13` 0.688557
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
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## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 0.518791
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 0.999754
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
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## `myCategory.fctr##:.clusterid.fctr14` 0.161807
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.999548
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 0.814672
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
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## `myCategory.fctr##:.clusterid.fctr15` 0.257700
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.692433
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 0.999729
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
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## `myCategory.fctr##:.clusterid.fctr16` 0.625954
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 0.388039
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 0.563950
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
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## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
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## `myCategory.fctr##:.clusterid.fctr17` 0.336124
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
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## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` < 2e-16
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 0.992396
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## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 0.999997
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## `myCategory.fctrCulture#Arts#:H.npnct19.log` 0.255195
## `myCategory.fctrForeign#World#:H.npnct19.log` 0.999996
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 0.999958
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 0.262710
## `myCategory.fctrmyOther:H.npnct19.log` < 2e-16
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 0.079518
## `myCategory.fctrScience#Health#:H.npnct19.log` 0.239817
## `myCategory.fctrStyles##Fashion:H.npnct19.log` < 2e-16
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 0.921912
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 0.999914
## `myCategory.fctrTStyle##:H.npnct19.log` < 2e-16
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 0.580499
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 0.999989
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 0.991420
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 0.998385
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 0.029120
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 0.999101
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 0.638595
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 0.004819
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 0.999993
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 0.383407
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 0.244586
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 0.715930
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 0.028941
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 0.804096
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 0.998580
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 0.180767
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 0.999963
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 0.991384
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 0.998230
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 0.323093
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 0.999101
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 0.993914
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 0.184026
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 0.999997
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 0.426661
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 0.188540
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 0.112485
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 0.039556
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 0.318353
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 0.998540
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 0.639726
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 0.999996
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 0.991436
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 0.998520
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 0.511884
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 0.999100
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 0.317204
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 0.050839
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 0.999996
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 0.286303
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 0.592751
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 0.052525
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 0.064150
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 0.744909
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 0.998558
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 0.123524
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 0.999951
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 0.991398
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 0.998166
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 0.052493
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 0.999099
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 0.310722
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 0.557516
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 0.999995
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 0.217225
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 0.181204
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 0.102959
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 0.064275
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 0.166903
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 0.998493
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 0.922386
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 0.999994
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 0.991486
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 0.998664
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 0.116944
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 0.999094
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 0.496939
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 0.747118
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.999998
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 0.285721
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 0.394248
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 0.138842
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 0.288706
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 0.766984
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 0.998520
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctr##:PubDate.wkend` 0.663789
## `myCategory.fctr#Multimedia#:PubDate.wkend` 0.999865
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` < 2e-16
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` 0.999866
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 0.999864
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 0.711784
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 0.373259
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 0.507106
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 7.85e-07
## `myCategory.fctrForeign#World#:PubDate.wkend` 0.999966
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 0.999197
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 0.912989
## `myCategory.fctrmyOther:PubDate.wkend` < 2e-16
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 0.030466
## `myCategory.fctrScience#Health#:PubDate.wkend` 0.552616
## `myCategory.fctrStyles##Fashion:PubDate.wkend` < 2e-16
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 0.474652
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 0.999964
## `myCategory.fctrTStyle##:PubDate.wkend` < 2e-16
## `myCategory.fctr##:H.npnct28.log` 0.709646
## `myCategory.fctr#Multimedia#:H.npnct28.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log` NA
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log` NA
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 0.999835
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log` NA
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct28.log` NA
## `myCategory.fctrCulture#Arts#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#:H.npnct28.log` NA
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 0.999879
## `myCategory.fctrmyOther:H.npnct28.log` NA
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log` NA
## `myCategory.fctrScience#Health#:H.npnct28.log` NA
## `myCategory.fctrStyles##Fashion:H.npnct28.log` NA
## `myCategory.fctrStyles#U.S.#:H.npnct28.log` NA
## `myCategory.fctrTravel#Travel#:H.npnct28.log` NA
## `myCategory.fctrTStyle##:H.npnct28.log` NA
##
## (Intercept) ***
## `myCategory.fctr#Multimedia#`
## `myCategory.fctr#Opinion#Room For Debate` ***
## `myCategory.fctr#Opinion#The Public Editor`
## `myCategory.fctr#U.S.#Education`
## `myCategory.fctrBusiness#Business Day#Dealbook` ***
## `myCategory.fctrBusiness#Business Day#Small Business` ***
## `myCategory.fctrBusiness#Crosswords/Games#`
## `myCategory.fctrBusiness#Technology#`
## `myCategory.fctrCulture#Arts#` **
## `myCategory.fctrForeign#World#`
## `myCategory.fctrForeign#World#Asia Pacific`
## `myCategory.fctrMetro#N.Y. / Region#` ***
## myCategory.fctrmyOther ***
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## `myCategory.fctrStyles##Fashion` ***
## `myCategory.fctrStyles#U.S.#` ***
## `myCategory.fctrTravel#Travel#`
## `myCategory.fctrTStyle##` ***
## WordCount.log.nonNA ***
## H.npnct19.log ***
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA`
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` ***
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA`
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA`
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` **
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` ***
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA`
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA`
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` ***
## `myCategory.fctrForeign#World#:WordCount.log.nonNA`
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` .
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## `myCategory.fctrmyOther:WordCount.log.nonNA` ***
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` ***
## `myCategory.fctrScience#Health#:WordCount.log.nonNA`
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` ***
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` ***
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA`
## `myCategory.fctrTStyle##:WordCount.log.nonNA` ***
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## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3`
## `myCategory.fctrForeign#World#:.clusterid.fctr3`
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3`
## `myCategory.fctrmyOther:.clusterid.fctr3`
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## `myCategory.fctr##:.clusterid.fctr4`
## `myCategory.fctr#Multimedia#:.clusterid.fctr4`
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## `myCategory.fctrForeign#World#:.clusterid.fctr5`
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr6`
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## `myCategory.fctr##:.clusterid.fctr7` .
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## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` .
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## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3`
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## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` .
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` ***
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3`
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## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` .
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4`
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## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4`
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## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5`
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## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` ***
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5`
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` ***
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` ***
## `myCategory.fctr##:PubDate.wkend`
## `myCategory.fctr#Multimedia#:PubDate.wkend`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` ***
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend`
## `myCategory.fctr#U.S.#Education:PubDate.wkend`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend`
## `myCategory.fctrBusiness#Technology#:PubDate.wkend`
## `myCategory.fctrCulture#Arts#:PubDate.wkend` ***
## `myCategory.fctrForeign#World#:PubDate.wkend`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend`
## `myCategory.fctrmyOther:PubDate.wkend` ***
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` *
## `myCategory.fctrScience#Health#:PubDate.wkend`
## `myCategory.fctrStyles##Fashion:PubDate.wkend` ***
## `myCategory.fctrStyles#U.S.#:PubDate.wkend`
## `myCategory.fctrTravel#Travel#:PubDate.wkend`
## `myCategory.fctrTStyle##:PubDate.wkend` ***
## `myCategory.fctr##:H.npnct28.log`
## `myCategory.fctr#Multimedia#:H.npnct28.log`
## `myCategory.fctr#Opinion#Room For Debate:H.npnct28.log`
## `myCategory.fctr#Opinion#The Public Editor:H.npnct28.log`
## `myCategory.fctr#U.S.#Education:H.npnct28.log`
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct28.log`
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct28.log`
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct28.log`
## `myCategory.fctrBusiness#Technology#:H.npnct28.log`
## `myCategory.fctrCulture#Arts#:H.npnct28.log`
## `myCategory.fctrForeign#World#:H.npnct28.log`
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct28.log`
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log`
## `myCategory.fctrmyOther:H.npnct28.log`
## `myCategory.fctrOpEd#Opinion#:H.npnct28.log`
## `myCategory.fctrScience#Health#:H.npnct28.log`
## `myCategory.fctrStyles##Fashion:H.npnct28.log`
## `myCategory.fctrStyles#U.S.#:H.npnct28.log`
## `myCategory.fctrTravel#Travel#:H.npnct28.log`
## `myCategory.fctrTStyle##:H.npnct28.log`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5900.1 on 6531 degrees of freedom
## Residual deviance: 4226.5 on 6285 degrees of freedom
## AIC: 4720.5
##
## Number of Fisher Scoring iterations: 25
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2866885
## 2 0.1 0.6814276
## 3 0.2 0.7396947
## 4 0.3 0.7530297
## 5 0.4 0.7536488
## 6 0.5 0.7511650
## 7 0.6 0.7371571
## 8 0.7 0.6681271
## 9 0.8 0.5649231
## 10 0.9 0.3230654
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Final.glm.N
## 1 N 5123
## 2 Y 241
## Popular.fctr.predict.Final.glm.Y
## 1 316
## 2 852
## Prediction
## Reference N Y
## N 5123 316
## Y 241 852
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.147275e-01 7.021577e-01 9.076914e-01 9.213907e-01 8.326699e-01
## AccuracyPValue McnemarPValue
## 4.951938e-83 1.715728e-03## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only
## model_id model_method
## 1 Final.glm glm
## feats
## 1 myCategory.fctr*WordCount.log.nonNA, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5, myCategory.fctr:PubDate.wkend, myCategory.fctr:H.npnct28.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 235.065 111.85
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9501971 0.4 0.7536488 0.8948246
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit min.aic.fit
## 1 0.9076914 0.9213907 0.6227899 4720.531
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01287157 0.03572412rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 1097.571 1342.225 244.654
## 15 fit.data.training 8 1 1342.225 NA NAglb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.5## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleadingsav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## Rank9.glm.importance
## `myCategory.fctrTStyle##` 2.284845e-06
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 9.605892e-07
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 5.121507e-08
## `myCategory.fctrTStyle##:PubDate.wkend` 1.963644e-08
## `myCategory.fctr#Opinion#Room For Debate` 5.120522e-08
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 8.519852e-16
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 1.839848e-15
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 1.831037e-15
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.598518e-16
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 1.502867e-06
## `myCategory.fctrBusiness#Business Day#Small Business` 8.241976e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 1.344872e-06
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 1.404121e-14
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 4.200071e-10
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 8.370014e-08
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 9.838308e-15
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 3.257174e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.289087e-07
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 3.478377e-15
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 3.775421e+01
## `myCategory.fctrmyOther:PubDate.wkend` 1.169430e-16
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 6.403830e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.259583e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 2.733009e-08
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 6.062123e+01
## `myCategory.fctrStyles##Fashion` 6.095459e-10
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 1.950169e-08
## myCategory.fctrmyOther 4.775128e-10
## `myCategory.fctrmyOther:H.npnct19.log` 2.644820e-10
## `myCategory.fctrmyOther:WordCount.log.nonNA` 8.882188e-10
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 1.064489e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 4.594471e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` 1.000000e+02
## `myCategory.fctrTStyle##:H.npnct19.log` 2.423739e-09
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 8.614427e-10
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 2.454686e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 2.255188e-08
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 8.115510e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 3.115215e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 3.147951e-14
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 0.000000e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 3.179932e-14
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 3.401713e-08
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 3.122164e-14
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 1.360716e-09
## WordCount.log.nonNA 2.922929e-05
## `myCategory.fctrOpEd#Opinion#` 2.948108e-05
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.346224e-05
## `myCategory.fctrStyles#U.S.#` 1.255605e-05
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 1.602796e-05
## `myCategory.fctr##:.clusterid.fctr2` 1.287989e-05
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 9.799118e-06
## H.npnct19.log 1.565273e-05
## `myCategory.fctrMetro#N.Y. / Region#` 1.388520e-05
## `myCategory.fctrBusiness#Business Day#Dealbook` 9.687334e-06
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 1.323422e-05
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 7.942242e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 8.556672e-06
## `myCategory.fctrCulture#Arts#` 6.415142e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 5.877979e-06
## `myCategory.fctr##:.clusterid.fctr10` 8.049864e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 6.875443e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 5.921093e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 7.880174e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 6.521922e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 7.451966e-06
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 2.666228e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 8.240205e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 2.479709e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 3.867666e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 6.619490e-06
## `myCategory.fctr##:.clusterid.fctr7` 6.514005e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 4.801099e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 6.268971e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 9.384577e-06
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 5.600610e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 5.865767e-06
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 4.702303e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 6.307971e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 6.348849e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 3.593473e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 3.848303e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 5.295195e-06
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 2.352542e-06
## `myCategory.fctr##:.clusterid.fctr14` 5.265397e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 8.330148e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 4.448832e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 6.264082e-06
## `myCategory.fctr##:.clusterid.fctr4` 3.584297e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 3.143170e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.110832e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 2.307417e-06
## `myCategory.fctr##:.clusterid.fctr5` 3.215400e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 4.576115e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 4.612341e-06
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 1.788485e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 5.370610e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 3.807336e-06
## `myCategory.fctrScience#Health#:H.npnct19.log` 4.488835e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 1.011597e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 1.230986e-06
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 3.026130e-07
## `myCategory.fctr##:.clusterid.fctr15` 1.829058e-06
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 1.510938e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.202017e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 4.932204e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 4.521026e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 2.789651e-06
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 3.934480e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 4.946152e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 2.874705e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 3.647051e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 6.039266e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 2.137184e-06
## `myCategory.fctr##:.clusterid.fctr8` 4.511615e-06
## `myCategory.fctr##:.clusterid.fctr17` 5.067304e-06
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 4.124067e-06
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 1.868903e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 4.106232e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 9.462511e-07
## `myCategory.fctr##:.clusterid.fctr12` 3.066250e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.683459e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 2.558353e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 3.484461e-06
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 1.443475e-08
## `myCategory.fctrForeign#World#Asia Pacific` 3.741685e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 2.384154e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 3.611779e-06
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 1.981669e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 3.688642e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 7.579186e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 3.595855e-06
## `myCategory.fctrScience#Health#:PubDate.wkend` 7.067731e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 6.009388e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 2.227300e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.427709e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 4.156516e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 3.250267e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 3.447424e-06
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 2.816553e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 1.631228e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.371392e-08
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.284071e-06
## `myCategory.fctr##:.clusterid.fctr16` 1.544245e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 3.906768e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.030820e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 3.866536e-06
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.594630e-06
## `myCategory.fctr##:PubDate.wkend` 8.100515e-07
## `myCategory.fctr##:.clusterid.fctr3` 1.099648e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.169486e-06
## `myCategory.fctr##:.clusterid.fctr13` 5.548774e-06
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 1.923278e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 8.980943e-07
## `myCategory.fctr##:H.npnct28.log` 3.048611e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 2.501662e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 8.831372e-08
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 4.935036e-06
## `myCategory.fctr##:.clusterid.fctr11` 1.066546e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 5.562643e-07
## `myCategory.fctr##:.clusterid.fctr6` 2.298298e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.173238e-06
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 1.880414e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 8.429663e-07
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 1.460159e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.966548e-06
## `myCategory.fctrBusiness#Technology#` 1.684421e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 1.803294e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 7.684549e-09
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 4.536787e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 7.371228e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 1.350735e-07
## `myCategory.fctrScience#Health#` 2.267185e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 3.157723e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 2.479624e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 2.250212e-07
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 2.072750e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 3.459515e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 6.309615e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 1.014805e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 9.142420e-07
## `myCategory.fctr#Opinion#The Public Editor` 3.912648e-11
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 3.084007e-11
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 2.601071e-11
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 1.520273e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 1.517043e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 1.756708e-10
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 3.679566e-09
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 3.462239e-06
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 2.060667e-09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 7.798167e-15
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 1.346338e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 9.013747e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 9.218443e-15
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 6.719480e-09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 8.725064e-15
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 6.617133e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 6.496246e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 6.569980e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 6.501329e-09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 6.286373e-15
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 3.481721e-08
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 1.818306e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 1.814126e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 1.813739e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 1.810528e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 1.803460e-10
## `myCategory.fctrBusiness#Crosswords/Games#` 1.800654e-10
## `myCategory.fctr#U.S.#Education` 3.892163e-10
## `myCategory.fctrTravel#Travel#` 1.068490e-09
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 1.050683e-08
## `myCategory.fctr##:.clusterid.fctr9` 2.376979e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 1.210140e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 1.332884e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 1.397598e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.361231e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 1.237475e-08
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 9.727372e-09
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 1.007279e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 9.146561e-09
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 7.363973e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 8.825715e-09
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 2.305464e-14
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 5.359319e-15
## `myCategory.fctr#Multimedia#:PubDate.wkend` 6.054326e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` NA
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 4.515547e-09
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 1.664927e-09
## `myCategory.fctr#Multimedia#` 1.884324e-09
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 1.561113e-09
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 1.148850e-09
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 1.191237e-09
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 1.487501e-09
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 8.248431e-09
## `myCategory.fctrForeign#World#:PubDate.wkend` 6.774083e-14
## `myCategory.fctrForeign#World#` 4.863020e-10
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 4.271925e-10
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 8.244360e-15
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 2.706821e-10
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 4.770371e-14
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 6.182610e-15
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 1.604906e-10
## `myCategory.fctrForeign#World#:H.npnct19.log` 3.078433e-10
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 4.121083e-14
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` NA
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 3.815601e-15
## importance
## `myCategory.fctrTStyle##` 1.000000e+02
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 7.837659e+01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 5.486278e+01
## `myCategory.fctrTStyle##:PubDate.wkend` 5.184820e+01
## `myCategory.fctr#Opinion#Room For Debate` 4.310726e+01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 4.281107e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 3.993233e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.609469e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.437178e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 3.163715e+01
## `myCategory.fctrBusiness#Business Day#Small Business` 2.910547e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 2.786863e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 2.129981e+01
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 2.107963e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 1.838143e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.772638e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 1.698803e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.686291e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 1.643184e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 1.532742e+01
## `myCategory.fctrmyOther:PubDate.wkend` 1.478378e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 1.333186e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.218110e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 1.151076e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 1.146784e+01
## `myCategory.fctrStyles##Fashion` 1.108835e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 1.061884e+01
## myCategory.fctrmyOther 1.039854e+01
## `myCategory.fctrmyOther:H.npnct19.log` 1.018993e+01
## `myCategory.fctrmyOther:WordCount.log.nonNA` 1.008287e+01
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 9.207735e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 8.988177e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` 8.806305e+00
## `myCategory.fctrTStyle##:H.npnct19.log` 8.675356e+00
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 6.786115e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 4.453252e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 3.635097e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 3.516277e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 1.945557e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.581134e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 1.308512e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.099928e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 8.549049e-01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 6.925538e-01
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 3.004919e-01
## WordCount.log.nonNA 3.566713e-06
## `myCategory.fctrOpEd#Opinion#` 3.437797e-06
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.587907e-06
## `myCategory.fctrStyles#U.S.#` 1.912123e-06
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 1.903762e-06
## `myCategory.fctr##:.clusterid.fctr2` 1.657311e-06
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 1.649874e-06
## H.npnct19.log 1.593365e-06
## `myCategory.fctrMetro#N.Y. / Region#` 1.455912e-06
## `myCategory.fctrBusiness#Business Day#Dealbook` 1.345989e-06
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 1.345482e-06
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 1.281813e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 1.254966e-06
## `myCategory.fctrCulture#Arts#` 1.125340e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 1.086536e-06
## `myCategory.fctr##:.clusterid.fctr10` 9.406888e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 9.040764e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 8.419366e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 8.409992e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 8.341049e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 7.933944e-07
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 7.795444e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 7.755804e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 7.527226e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 7.474136e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 7.473132e-07
## `myCategory.fctr##:.clusterid.fctr7` 7.218368e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 7.135219e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 7.131851e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 6.781506e-07
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 6.758829e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 6.573352e-07
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 6.445953e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 6.285436e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 6.117557e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 6.042813e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 5.936514e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 5.705108e-07
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 5.485561e-07
## `myCategory.fctr##:.clusterid.fctr14` 5.392525e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 5.367101e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 5.357326e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 5.327781e-07
## `myCategory.fctr##:.clusterid.fctr4` 5.314840e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 5.158852e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 5.153693e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 5.120551e-07
## `myCategory.fctr##:.clusterid.fctr5` 5.103197e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 5.094711e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 5.068323e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 4.828130e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 4.756228e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 4.742299e-07
## `myCategory.fctrScience#Health#:H.npnct19.log` 4.530753e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 4.519356e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 4.485101e-07
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 4.385737e-07
## `myCategory.fctr##:.clusterid.fctr15` 4.362696e-07
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 4.317077e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.278492e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 4.114890e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 4.109930e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 4.089487e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 4.013683e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 3.907341e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 3.855351e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 3.846202e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 3.808715e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 3.797559e-07
## `myCategory.fctr##:.clusterid.fctr8` 3.720519e-07
## `myCategory.fctr##:.clusterid.fctr17` 3.707454e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 3.456772e-07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 3.431991e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 3.359718e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 3.327124e-07
## `myCategory.fctr##:.clusterid.fctr12` 3.307825e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.283800e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 3.109628e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 3.064012e-07
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 2.755694e-07
## `myCategory.fctrForeign#World#Asia Pacific` 2.628488e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 2.618412e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 2.611478e-07
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 2.556883e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 2.528193e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 2.486961e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.299866e-07
## `myCategory.fctrScience#Health#:PubDate.wkend` 2.288969e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 2.275094e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 2.263094e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.260798e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 2.223980e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 2.174786e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 2.130207e-07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 2.121750e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 2.061592e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.996518e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.981383e-07
## `myCategory.fctr##:.clusterid.fctr16` 1.878795e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.865807e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.810318e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.804215e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.677761e-07
## `myCategory.fctr##:PubDate.wkend` 1.675502e-07
## `myCategory.fctr##:.clusterid.fctr3` 1.626328e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.580509e-07
## `myCategory.fctr##:.clusterid.fctr13` 1.544931e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 1.532033e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.524663e-07
## `myCategory.fctr##:H.npnct28.log` 1.435146e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 1.424081e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 1.408366e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.402664e-07
## `myCategory.fctr##:.clusterid.fctr11` 1.372074e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 1.254091e-07
## `myCategory.fctr##:.clusterid.fctr6` 1.252376e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.242848e-07
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 1.195384e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 1.142168e-07
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 1.094158e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.046234e-07
## `myCategory.fctrBusiness#Technology#` 9.757127e-08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 9.561025e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 9.035012e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 7.879616e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 7.020506e-08
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 5.774077e-08
## `myCategory.fctrScience#Health#` 5.642133e-08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 4.211730e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 3.863492e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 3.799885e-08
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 3.778324e-08
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 3.755332e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 2.195274e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 2.018071e-08
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 1.624747e-08
## `myCategory.fctr#Opinion#The Public Editor` 4.170625e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 4.161807e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 4.154666e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 4.144475e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 4.136567e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 4.112374e-09
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 3.672539e-09
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 2.939185e-09
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 9.823238e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 8.854467e-10
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 8.764559e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 8.541457e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 7.792280e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 7.274620e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 7.144203e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 7.140214e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 7.044608e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 6.958738e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 6.851945e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 6.444580e-10
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 5.542535e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 4.367209e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 4.343860e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 4.341100e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 4.337978e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 4.335122e-10
## `myCategory.fctrBusiness#Crosswords/Games#` 4.333645e-10
## `myCategory.fctr#U.S.#Education` 4.295877e-10
## `myCategory.fctrTravel#Travel#` 4.157460e-10
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 3.873939e-10
## `myCategory.fctr##:.clusterid.fctr9` 3.026444e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 2.216970e-10
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 2.176956e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 2.121290e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.848431e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 1.586154e-10
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 1.535767e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 1.425362e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 1.304069e-10
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.186972e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.178777e-10
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 7.881611e-11
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 6.489696e-11
## `myCategory.fctr#Multimedia#:PubDate.wkend` 6.468341e-11
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` 6.418581e-11
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 5.752816e-11
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 4.103606e-11
## `myCategory.fctr#Multimedia#` 2.932612e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 2.317092e-11
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 1.947941e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 1.730219e-11
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 1.714009e-11
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 1.660105e-11
## `myCategory.fctrForeign#World#:PubDate.wkend` 1.558360e-11
## `myCategory.fctrForeign#World#` 1.279560e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 4.454489e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 2.625920e-12
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 2.403129e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 1.699658e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 1.306532e-12
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 1.191574e-12
## `myCategory.fctrForeign#World#:H.npnct19.log` 1.180297e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 9.264147e-13
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 4.852603e-13
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000000e+00
## Final.glm.importance
## `myCategory.fctrTStyle##` 1.000000e+02
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 7.837659e+01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 5.486278e+01
## `myCategory.fctrTStyle##:PubDate.wkend` 5.184820e+01
## `myCategory.fctr#Opinion#Room For Debate` 4.310726e+01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 4.281107e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 3.993233e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.609469e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.437178e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 3.163715e+01
## `myCategory.fctrBusiness#Business Day#Small Business` 2.910547e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 2.786863e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 2.129981e+01
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 2.107963e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 1.838143e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.772638e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 1.698803e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.686291e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 1.643184e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 1.532742e+01
## `myCategory.fctrmyOther:PubDate.wkend` 1.478378e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 1.333186e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.218110e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 1.151076e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 1.146784e+01
## `myCategory.fctrStyles##Fashion` 1.108835e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 1.061884e+01
## myCategory.fctrmyOther 1.039854e+01
## `myCategory.fctrmyOther:H.npnct19.log` 1.018993e+01
## `myCategory.fctrmyOther:WordCount.log.nonNA` 1.008287e+01
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 9.207735e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 8.988177e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` 8.806305e+00
## `myCategory.fctrTStyle##:H.npnct19.log` 8.675356e+00
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 6.786115e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 4.453252e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 3.635097e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 3.516277e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 1.945557e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.581134e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 1.308512e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.099928e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 8.549049e-01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 6.925538e-01
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 3.004919e-01
## WordCount.log.nonNA 3.566713e-06
## `myCategory.fctrOpEd#Opinion#` 3.437797e-06
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.587907e-06
## `myCategory.fctrStyles#U.S.#` 1.912123e-06
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 1.903762e-06
## `myCategory.fctr##:.clusterid.fctr2` 1.657311e-06
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 1.649874e-06
## H.npnct19.log 1.593365e-06
## `myCategory.fctrMetro#N.Y. / Region#` 1.455912e-06
## `myCategory.fctrBusiness#Business Day#Dealbook` 1.345989e-06
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 1.345482e-06
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 1.281813e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 1.254966e-06
## `myCategory.fctrCulture#Arts#` 1.125340e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 1.086536e-06
## `myCategory.fctr##:.clusterid.fctr10` 9.406888e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 9.040764e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 8.419366e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 8.409992e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 8.341049e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 7.933944e-07
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 7.795444e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 7.755804e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 7.527226e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 7.474136e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 7.473132e-07
## `myCategory.fctr##:.clusterid.fctr7` 7.218368e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 7.135219e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 7.131851e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 6.781506e-07
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 6.758829e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 6.573352e-07
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 6.445953e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 6.285436e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 6.117557e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 6.042813e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 5.936514e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 5.705108e-07
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 5.485561e-07
## `myCategory.fctr##:.clusterid.fctr14` 5.392525e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 5.367101e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 5.357326e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 5.327781e-07
## `myCategory.fctr##:.clusterid.fctr4` 5.314840e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 5.158852e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 5.153693e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 5.120551e-07
## `myCategory.fctr##:.clusterid.fctr5` 5.103197e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 5.094711e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 5.068323e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 4.828130e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 4.756228e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 4.742299e-07
## `myCategory.fctrScience#Health#:H.npnct19.log` 4.530753e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 4.519356e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 4.485101e-07
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 4.385737e-07
## `myCategory.fctr##:.clusterid.fctr15` 4.362696e-07
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 4.317077e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.278492e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 4.114890e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 4.109930e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 4.089487e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 4.013683e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 3.907341e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 3.855351e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 3.846202e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 3.808715e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 3.797559e-07
## `myCategory.fctr##:.clusterid.fctr8` 3.720519e-07
## `myCategory.fctr##:.clusterid.fctr17` 3.707454e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 3.456772e-07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 3.431991e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 3.359718e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 3.327124e-07
## `myCategory.fctr##:.clusterid.fctr12` 3.307825e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.283800e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 3.109628e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 3.064012e-07
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 2.755694e-07
## `myCategory.fctrForeign#World#Asia Pacific` 2.628488e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 2.618412e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 2.611478e-07
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 2.556883e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 2.528193e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 2.486961e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.299866e-07
## `myCategory.fctrScience#Health#:PubDate.wkend` 2.288969e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 2.275094e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 2.263094e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.260798e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 2.223980e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 2.174786e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 2.130207e-07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 2.121750e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 2.061592e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.996518e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.981383e-07
## `myCategory.fctr##:.clusterid.fctr16` 1.878795e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.865807e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.810318e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.804215e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.677761e-07
## `myCategory.fctr##:PubDate.wkend` 1.675502e-07
## `myCategory.fctr##:.clusterid.fctr3` 1.626328e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.580509e-07
## `myCategory.fctr##:.clusterid.fctr13` 1.544931e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 1.532033e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.524663e-07
## `myCategory.fctr##:H.npnct28.log` 1.435146e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 1.424081e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 1.408366e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.402664e-07
## `myCategory.fctr##:.clusterid.fctr11` 1.372074e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 1.254091e-07
## `myCategory.fctr##:.clusterid.fctr6` 1.252376e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.242848e-07
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 1.195384e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 1.142168e-07
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 1.094158e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.046234e-07
## `myCategory.fctrBusiness#Technology#` 9.757127e-08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 9.561025e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 9.035012e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 7.879616e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 7.020506e-08
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 5.774077e-08
## `myCategory.fctrScience#Health#` 5.642133e-08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 4.211730e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 3.863492e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 3.799885e-08
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 3.778324e-08
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 3.755332e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 2.195274e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 2.018071e-08
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 1.624747e-08
## `myCategory.fctr#Opinion#The Public Editor` 4.170625e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 4.161807e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 4.154666e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 4.144475e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 4.136567e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 4.112374e-09
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 3.672539e-09
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 2.939185e-09
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 9.823238e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 8.854467e-10
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 8.764559e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 8.541457e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 7.792280e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 7.274620e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 7.144203e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 7.140214e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 7.044608e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 6.958738e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 6.851945e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 6.444580e-10
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 5.542535e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 4.367209e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 4.343860e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 4.341100e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 4.337978e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 4.335122e-10
## `myCategory.fctrBusiness#Crosswords/Games#` 4.333645e-10
## `myCategory.fctr#U.S.#Education` 4.295877e-10
## `myCategory.fctrTravel#Travel#` 4.157460e-10
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 3.873939e-10
## `myCategory.fctr##:.clusterid.fctr9` 3.026444e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 2.216970e-10
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 2.176956e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 2.121290e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.848431e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 1.586154e-10
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 1.535767e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 1.425362e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 1.304069e-10
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.186972e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.178777e-10
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 7.881611e-11
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 6.489696e-11
## `myCategory.fctr#Multimedia#:PubDate.wkend` 6.468341e-11
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` 6.418581e-11
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 5.752816e-11
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 4.103606e-11
## `myCategory.fctr#Multimedia#` 2.932612e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 2.317092e-11
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 1.947941e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 1.730219e-11
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 1.714009e-11
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 1.660105e-11
## `myCategory.fctrForeign#World#:PubDate.wkend` 1.558360e-11
## `myCategory.fctrForeign#World#` 1.279560e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 4.454489e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 2.625920e-12
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 2.403129e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 1.699658e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 1.306532e-12
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 1.191574e-12
## `myCategory.fctrForeign#World#:H.npnct19.log` 1.180297e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 9.264147e-13
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 4.852603e-13
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000000e+00if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 13
## [1] "Min/Max Boundaries: "
## [1] UniqueID
## [2] Popular.fctr
## [3] Popular.fctr.predict.Final.glm.prob
## [4] Popular.fctr.predict.Final.glm
## [5] Popular.fctr.predict.Final.glm.accurate
## [6] Popular.fctr.predict.Final.glm.error
## [7] .label
## <0 rows> (or 0-length row.names)
## [1] "Inaccurate: "
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## 360 360 Y 2.220446e-16
## 693 693 Y 2.220446e-16
## 1255 1255 Y 2.220446e-16
## 2182 2182 Y 2.220446e-16
## 2538 2538 Y 2.220446e-16
## 2776 2776 Y 2.220446e-16
## Popular.fctr.predict.Final.glm
## 360 N
## 693 N
## 1255 N
## 2182 N
## 2538 N
## 2776 N
## Popular.fctr.predict.Final.glm.accurate
## 360 FALSE
## 693 FALSE
## 1255 FALSE
## 2182 FALSE
## 2538 FALSE
## 2776 FALSE
## Popular.fctr.predict.Final.glm.error
## 360 -0.5
## 693 -0.5
## 1255 -0.5
## 2182 -0.5
## 2538 -0.5
## 2776 -0.5
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## 3554 3554 Y 0.03319932
## 2202 2202 Y 0.43828654
## 4177 4177 N 0.54074445
## 4960 4960 N 0.74819407
## 6276 6276 N 0.84135522
## 3308 3308 N 1.00000000
## Popular.fctr.predict.Final.glm
## 3554 N
## 2202 N
## 4177 Y
## 4960 Y
## 6276 Y
## 3308 Y
## Popular.fctr.predict.Final.glm.accurate
## 3554 FALSE
## 2202 FALSE
## 4177 FALSE
## 4960 FALSE
## 6276 FALSE
## 3308 FALSE
## Popular.fctr.predict.Final.glm.error
## 3554 -0.46680068
## 2202 -0.06171346
## 4177 0.04074445
## 4960 0.24819407
## 6276 0.34135522
## 3308 0.50000000
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## 3504 3504 N 1
## 3860 3860 N 1
## 4004 4004 N 1
## 4702 4702 N 1
## 5487 5487 N 1
## 5891 5891 N 1
## Popular.fctr.predict.Final.glm
## 3504 Y
## 3860 Y
## 4004 Y
## 4702 Y
## 5487 Y
## 5891 Y
## Popular.fctr.predict.Final.glm.accurate
## 3504 FALSE
## 3860 FALSE
## 4004 FALSE
## 4702 FALSE
## 5487 FALSE
## 5891 FALSE
## Popular.fctr.predict.Final.glm.error
## 3504 0.5
## 3860 0.5
## 4004 0.5
## 4702 0.5
## 5487 0.5
## 5891 0.5
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])## Popular.fctr Popular.fctr.predict.Final.glm.prob
## 92 Y 3.615058e-03
## 693 Y 2.220446e-16
## 4020 Y 3.015849e-02
## 4721 Y 4.321461e-02
## Popular.fctr.predict.Final.glm
## 92 N
## 693 N
## 4020 N
## 4721 Nprint(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "Popular.fctr.predict.Final.glm.prob"
## [2] "Popular.fctr.predict.Final.glm"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 1342.225 1355.019 12.794
## 16 predict.data.new 9 0 1355.019 NA NAStep 9.0: predict data new
# Compute final model predictions
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(glb_newobs_df, mdl_id = glb_fin_mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.5## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleadingif (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 13## Warning in loop_apply(n, do.ply): no non-missing arguments to min;
## returning Inf## Warning in loop_apply(n, do.ply): no non-missing arguments to max;
## returning -Inf## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).
## Warning in loop_apply(n, do.ply): no non-missing arguments to min;
## returning Inf## Warning in loop_apply(n, do.ply): no non-missing arguments to max;
## returning -Inf## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).
## Warning in loop_apply(n, do.ply): no non-missing arguments to min;
## returning Inf## Warning in loop_apply(n, do.ply): no non-missing arguments to max;
## returning -Inf## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).
## Warning in loop_apply(n, do.ply): no non-missing arguments to min;
## returning Inf## Warning in loop_apply(n, do.ply): no non-missing arguments to max;
## returning -Inf## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).
## Warning in loop_apply(n, do.ply): no non-missing arguments to min;
## returning Inf## Warning in loop_apply(n, do.ply): no non-missing arguments to max;
## returning -Inf## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).
## [1] "Min/Max Boundaries: "
## [1] UniqueID
## [2] Popular.fctr
## [3] Popular.fctr.predict.Final.glm.prob
## [4] Popular.fctr.predict.Final.glm
## [5] Popular.fctr.predict.Final.glm.accurate
## [6] Popular.fctr.predict.Final.glm.error
## [7] .label
## <0 rows> (or 0-length row.names)
## [1] "Inaccurate: "
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## NA NA <NA> NA
## NA.1 NA <NA> NA
## NA.2 NA <NA> NA
## NA.3 NA <NA> NA
## NA.4 NA <NA> NA
## NA.5 NA <NA> NA
## Popular.fctr.predict.Final.glm
## NA <NA>
## NA.1 <NA>
## NA.2 <NA>
## NA.3 <NA>
## NA.4 <NA>
## NA.5 <NA>
## Popular.fctr.predict.Final.glm.accurate
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## Popular.fctr.predict.Final.glm.error
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## NA.168 NA <NA> NA
## NA.243 NA <NA> NA
## NA.261 NA <NA> NA
## NA.1464 NA <NA> NA
## NA.1493 NA <NA> NA
## NA.1501 NA <NA> NA
## Popular.fctr.predict.Final.glm
## NA.168 <NA>
## NA.243 <NA>
## NA.261 <NA>
## NA.1464 <NA>
## NA.1493 <NA>
## NA.1501 <NA>
## Popular.fctr.predict.Final.glm.accurate
## NA.168 NA
## NA.243 NA
## NA.261 NA
## NA.1464 NA
## NA.1493 NA
## NA.1501 NA
## Popular.fctr.predict.Final.glm.error
## NA.168 NA
## NA.243 NA
## NA.261 NA
## NA.1464 NA
## NA.1493 NA
## NA.1501 NA
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## NA.1864 NA <NA> NA
## NA.1865 NA <NA> NA
## NA.1866 NA <NA> NA
## NA.1867 NA <NA> NA
## NA.1868 NA <NA> NA
## NA.1869 NA <NA> NA
## Popular.fctr.predict.Final.glm
## NA.1864 <NA>
## NA.1865 <NA>
## NA.1866 <NA>
## NA.1867 <NA>
## NA.1868 <NA>
## NA.1869 <NA>
## Popular.fctr.predict.Final.glm.accurate
## NA.1864 NA
## NA.1865 NA
## NA.1866 NA
## NA.1867 NA
## NA.1868 NA
## NA.1869 NA
## Popular.fctr.predict.Final.glm.error
## NA.1864 NA
## NA.1865 NA
## NA.1866 NA
## NA.1867 NA
## NA.1868 NA
## NA.1869 NA## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_vars,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
} else submit_df <- glb_newobs_df[, c(glb_id_vars,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
write.csv(submit_df,
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv"), row.names=FALSE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.5print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: Rank9.glm"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.glm"print(dim(glb_fitobs_df))## [1] 4475 290print(dsp_models_df)## model_id max.Accuracy.OOB
## Rank9.glm Rank9.glm 0.9139524
## Rank9.bayesglm Rank9.bayesglm 0.9124939
## All.X.bayesglm All.X.bayesglm 0.9052018
## All.X.2.glm All.X.2.glm 0.9003403
## All.X.glm All.X.glm 0.8954789
## Low.cor.X.glm Low.cor.X.glm 0.8949927
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart 0.8862421
## MFO.myMFO_classfr MFO.myMFO_classfr 0.8327662
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart 0.8327662
## Max.cor.Y.TmSrs.poly.glm Max.cor.Y.TmSrs.poly.glm 0.7574137
## Max.cor.Y.glm Max.cor.Y.glm 0.7525523
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart 0.7481770
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm 0.7476908
## Max.cor.Y.rpart Max.cor.Y.rpart 0.6514341
## Random.myrandom_classfr Random.myrandom_classfr 0.1672338
## max.auc.OOB max.Kappa.OOB min.aic.fit
## Rank9.glm 0.9337182 0.6907095 2344.307
## Rank9.bayesglm 0.9420149 0.6922670 2738.562
## All.X.bayesglm 0.9331243 0.6584619 2669.460
## All.X.2.glm 0.9303853 0.6622096 2148.969
## All.X.glm 0.9275750 0.6480478 2104.317
## Low.cor.X.glm 0.9281045 0.6444742 2092.229
## All.X.no.rnorm.rpart 0.7084504 0.5054039 NA
## MFO.myMFO_classfr 0.5000000 0.0000000 NA
## Max.cor.Y.cv.0.rpart 0.5000000 0.0000000 NA
## Max.cor.Y.TmSrs.poly.glm 0.7529375 0.2831146 3456.116
## Max.cor.Y.glm 0.7287696 0.2631116 3592.781
## Max.cor.Y.cv.0.cp.0.rpart 0.7031303 0.2508040 NA
## Interact.High.cor.Y.glm 0.7921045 0.3334126 3296.412
## Max.cor.Y.rpart 0.6818422 0.2100966 NA
## Random.myrandom_classfr 0.4877001 0.0000000 NA
## opt.prob.threshold.OOB
## Rank9.glm 0.5
## Rank9.bayesglm 0.4
## All.X.bayesglm 0.4
## All.X.2.glm 0.3
## All.X.glm 0.3
## Low.cor.X.glm 0.3
## All.X.no.rnorm.rpart 0.7
## MFO.myMFO_classfr 0.5
## Max.cor.Y.cv.0.rpart 0.5
## Max.cor.Y.TmSrs.poly.glm 0.2
## Max.cor.Y.glm 0.2
## Max.cor.Y.cv.0.cp.0.rpart 0.2
## Interact.High.cor.Y.glm 0.2
## Max.cor.Y.rpart 0.2
## Random.myrandom_classfr 0.1if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
tmp_OOBobs_df <- glb_OOBobs_df[, c("myCategory", predct_accurate_var_name)]
names(tmp_OOBobs_df)[2] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
} ## [1] "Rank9.glm OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 1625 88
## Y 89 255
## myCategory .n.OOB .n.Tst .freqRatio.Tst
## 1 ## 410 342 0.182887701
## 15 OpEd#Opinion# 154 164 0.087700535
## 6 Business#Business Day#Dealbook 312 304 0.162566845
## 18 Styles#U.S.# 54 61 0.032620321
## 9 Business#Technology# 114 114 0.060962567
## 10 Culture#Arts# 225 244 0.130481283
## 16 Science#Health# 66 57 0.030481283
## 3 #Opinion#Room For Debate 18 20 0.010695187
## 8 Business#Crosswords/Games# 40 42 0.022459893
## 4 #Opinion#The Public Editor 10 10 0.005347594
## 7 Business#Business Day#Small Business 45 41 0.021925134
## 13 Metro#N.Y. / Region# 60 67 0.035828877
## 20 TStyle## 221 105 0.056149733
## 12 Foreign#World#Asia Pacific 61 56 0.029946524
## 17 Styles##Fashion 41 15 0.008021390
## 19 Travel#Travel# 31 35 0.018716578
## 2 #Multimedia# 42 52 0.027807487
## 5 #U.S.#Education 93 89 0.047593583
## 11 Foreign#World# 47 47 0.025133690
## 14 myOther 13 5 0.002673797
## .freqRatio.OOB accurate.OOB.FALSE accurate.OOB.TRUE max.accuracy.OOB
## 1 0.199319397 38 372 0.9073171
## 15 0.074866310 33 121 0.7857143
## 6 0.151677200 31 281 0.9006410
## 18 0.026251823 21 33 0.6111111
## 9 0.055420515 13 101 0.8859649
## 10 0.109382596 13 212 0.9422222
## 16 0.032085561 8 58 0.8787879
## 3 0.008750608 4 14 0.7777778
## 8 0.019445795 4 36 0.9000000
## 4 0.004861449 3 7 0.7000000
## 7 0.021876519 2 43 0.9555556
## 13 0.029168692 2 58 0.9666667
## 20 0.107438017 2 219 0.9909502
## 12 0.029654837 1 60 0.9836066
## 17 0.019931940 1 40 0.9756098
## 19 0.015070491 1 30 0.9677419
## 2 0.020418085 0 42 1.0000000
## 5 0.045211473 0 93 1.0000000
## 11 0.022848809 0 47 1.0000000
## 14 0.006319883 0 13 1.0000000dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_vars]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
if (glb_is_classification) {
print("FN_OOB_ids:")
print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
glb_txt_vars])
print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_OOBobs_df),
grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
}## [1] "FN_OOB_ids:"
## [1] Popular.fctr
## [2] Popular.fctr.predict.Rank9.glm.prob
## [3] Popular.fctr.predict.Rank9.glm
## [4] Popular.fctr.predict.Rank9.glm.accurate
## <0 rows> (or 0-length row.names)
## [1] Headline Snippet Abstract
## <0 rows> (or 0-length row.names)
## PubDate.POSIX H.T.X2014
## 0 0
## H.T.X2015 H.T.art
## 0 0
## H.T.bank H.T.big
## 0 0
## H.T.billion H.T.busi
## 0 0
## H.T.china H.T.daili
## 0 0
## H.T.day H.T.deal
## 0 0
## H.T.fashion H.T.first
## 0 0
## H.T.make H.T.morn
## 0 0
## H.T.new H.T.news
## 0 0
## H.T.newyork H.T.obama
## 0 0
## H.T.pictur H.T.polit
## 0 0
## H.T.report H.T.say
## 0 0
## H.T.springsumm H.T.take
## 0 0
## H.T.test H.T.today
## 0 0
## H.T.week S.T.articl
## 0 0
## S.T.can S.T.compani
## 0 0
## S.T.day S.T.fashion
## 0 0
## S.T.first S.T.intern
## 0 0
## S.T.make S.T.new
## 0 0
## S.T.newyork S.T.newyorktim
## 0 0
## S.T.one S.T.presid
## 0 0
## S.T.report S.T.said
## 0 0
## S.T.share S.T.show
## 0 0
## S.T.take S.T.time
## 0 0
## S.T.week S.T.will
## 0 0
## S.T.year A.T.articl
## 0 0
## A.T.can A.T.compani
## 0 0
## A.T.day A.T.fashion
## 0 0
## A.T.first A.T.intern
## 0 0
## A.T.make A.T.new
## 0 0
## A.T.newyork A.T.newyorktim
## 0 0
## A.T.one A.T.presid
## 0 0
## A.T.report A.T.said
## 0 0
## A.T.share A.T.show
## 0 0
## A.T.take A.T.time
## 0 0
## A.T.week A.T.will
## 0 0
## A.T.year H.T.clip
## 0 0
## H.T.ebola H.T.get
## 0 0
## H.T.newyorktim H.T.read
## 0 0
## H.T.word H.nwrds.log
## 0 0
## H.nwrds.unq.log H.sum.TfIdf
## 0 0
## H.ratio.sum.TfIdf.nwrds H.nchrs.log
## 0 0
## H.nuppr.log H.ndgts.log
## 0 0
## H.npnct01.log H.npnct02.log
## 0 0
## H.npnct03.log H.npnct04.log
## 0 0
## H.npnct05.log H.npnct06.log
## 0 0
## H.npnct07.log H.npnct08.log
## 0 0
## H.npnct09.log H.npnct10.log
## 0 0
## H.npnct11.log H.npnct12.log
## 0 0
## H.npnct13.log H.npnct14.log
## 0 0
## H.npnct15.log H.npnct16.log
## 0 0
## H.npnct17.log H.npnct18.log
## 0 0
## H.npnct19.log H.npnct20.log
## 0 0
## H.npnct21.log H.npnct22.log
## 0 0
## H.npnct23.log H.npnct24.log
## 0 0
## H.npnct25.log H.npnct26.log
## 0 0
## H.npnct27.log H.npnct28.log
## 0 0
## H.npnct29.log H.npnct30.log
## 0 0
## H.nstopwrds.log H.ratio.nstopwrds.nwrds
## 0 0
## H.P.http H.P.year.colon
## 0 0
## H.P.daily.clip.report H.P.fashion.week
## 0 0
## H.P.first.draft H.P.facts.figures
## 0 0
## H.P.friday.night.music H.P.no.comment.colon
## 0 0
## H.P.on.this.day H.P.quandary
## 0 0
## H.P.readers.respond H.P.recap.colon
## 0 0
## H.P.s.notebook H.P.today.in.politic
## 0 0
## H.P.today.in.smallbusiness H.P.verbatim.colon
## 0 0
## H.P.what.we.are S.T.appear
## 0 0
## S.T.archiv S.T.diari
## 0 0
## S.T.editor S.T.herald
## 0 0
## S.T.obama S.T.photo
## 0 0
## S.T.senat S.T.tribun
## 0 0
## S.T.word S.nwrds.log
## 0 0
## S.nwrds.unq.log S.sum.TfIdf
## 0 0
## S.ratio.sum.TfIdf.nwrds S.nchrs.log
## 0 0
## S.nuppr.log S.ndgts.log
## 0 0
## S.npnct01.log S.npnct02.log
## 0 0
## S.npnct03.log S.npnct04.log
## 0 0
## S.npnct05.log S.npnct06.log
## 0 0
## S.npnct07.log S.npnct08.log
## 0 0
## S.npnct09.log S.npnct10.log
## 0 0
## S.npnct11.log S.npnct12.log
## 0 0
## S.npnct13.log S.npnct14.log
## 0 0
## S.npnct15.log S.npnct16.log
## 0 0
## S.npnct17.log S.npnct18.log
## 0 0
## S.npnct19.log S.npnct20.log
## 0 0
## S.npnct21.log S.npnct22.log
## 0 0
## S.npnct23.log S.npnct24.log
## 0 0
## S.npnct25.log S.npnct26.log
## 0 0
## S.npnct27.log S.npnct28.log
## 0 0
## S.npnct29.log S.npnct30.log
## 0 0
## S.nstopwrds.log S.ratio.nstopwrds.nwrds
## 0 0
## S.P.http S.P.year.colon
## 0 0
## S.P.daily.clip.report S.P.fashion.week
## 0 0
## S.P.first.draft S.P.metropolitan.diary.colon
## 0 0
## A.T.appear A.T.archiv
## 0 0
## A.T.diari A.T.editor
## 0 0
## A.T.herald A.T.obama
## 0 0
## A.T.photo A.T.senat
## 0 0
## A.T.tribun A.T.word
## 0 0
## A.nwrds.log A.nwrds.unq.log
## 0 0
## A.sum.TfIdf A.ratio.sum.TfIdf.nwrds
## 0 0
## A.nchrs.log A.nuppr.log
## 0 0
## A.ndgts.log A.npnct01.log
## 0 0
## A.npnct02.log A.npnct03.log
## 0 0
## A.npnct04.log A.npnct05.log
## 0 0
## A.npnct06.log A.npnct07.log
## 0 0
## A.npnct08.log A.npnct09.log
## 0 0
## A.npnct10.log A.npnct11.log
## 0 0
## A.npnct12.log A.npnct13.log
## 0 0
## A.npnct14.log A.npnct15.log
## 0 0
## A.npnct16.log A.npnct17.log
## 0 0
## A.npnct18.log A.npnct19.log
## 0 0
## A.npnct20.log A.npnct21.log
## 0 0
## A.npnct22.log A.npnct23.log
## 0 0
## A.npnct24.log A.npnct25.log
## 0 0
## A.npnct26.log A.npnct27.log
## 0 0
## A.npnct28.log A.npnct29.log
## 0 0
## A.npnct30.log A.nstopwrds.log
## 0 0
## A.ratio.nstopwrds.nwrds A.P.http
## 0 0
## A.P.year.colon A.P.daily.clip.report
## 0 0
## A.P.fashion.week A.P.first.draft
## 0 0
## A.P.metropolitan.diary.colon
## 0dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## Rank9.glm.importance
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` 1.000000e+02
## `myCategory.fctrBusiness#Business Day#Small Business` 8.241976e+01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 8.115510e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 6.403830e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 6.062123e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 4.594471e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 3.775421e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 3.257174e+01
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 1.064489e+01
## `myCategory.fctrOpEd#Opinion#` 2.948108e-05
## WordCount.log.nonNA 2.922929e-05
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.346224e-05
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 1.602796e-05
## H.npnct19.log 1.565273e-05
## `myCategory.fctrMetro#N.Y. / Region#` 1.388520e-05
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 1.323422e-05
## `myCategory.fctr##:.clusterid.fctr2` 1.287989e-05
## `myCategory.fctrStyles#U.S.#` 1.255605e-05
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 9.799118e-06
## `myCategory.fctrBusiness#Business Day#Dealbook` 9.687334e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 9.384577e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 8.556672e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 8.330148e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 8.240205e-06
## `myCategory.fctr##:.clusterid.fctr10` 8.049864e-06
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 7.942242e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 7.880174e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 7.451966e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 6.875443e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 6.619490e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 6.521922e-06
## `myCategory.fctr##:.clusterid.fctr7` 6.514005e-06
## `myCategory.fctrCulture#Arts#` 6.415142e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 6.348849e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 6.307971e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 6.268971e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 6.264082e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 6.039266e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 5.921093e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 5.877979e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 5.865767e-06
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 5.600610e-06
## `myCategory.fctr##:.clusterid.fctr13` 5.548774e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 5.370610e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 5.295195e-06
## `myCategory.fctr##:.clusterid.fctr14` 5.265397e-06
## `myCategory.fctr##:.clusterid.fctr17` 5.067304e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 4.946152e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 4.935036e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 4.932204e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 4.801099e-06
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 4.702303e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 4.612341e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 4.576115e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 4.521026e-06
## `myCategory.fctr##:.clusterid.fctr8` 4.511615e-06
## `myCategory.fctrScience#Health#:H.npnct19.log` 4.488835e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 4.448832e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.202017e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 4.156516e-06
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 4.124067e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 4.106232e-06
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 3.934480e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 3.906768e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 3.867666e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 3.866536e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 3.848303e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 3.807336e-06
## `myCategory.fctrForeign#World#Asia Pacific` 3.741685e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 3.688642e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 3.647051e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 3.611779e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 3.595855e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 3.593473e-06
## `myCategory.fctr##:.clusterid.fctr4` 3.584297e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 3.484461e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 3.462239e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 3.459515e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 3.447424e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 3.250267e-06
## `myCategory.fctr##:.clusterid.fctr5` 3.215400e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 3.157723e-06
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 3.143170e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.110832e-06
## `myCategory.fctr##:.clusterid.fctr12` 3.066250e-06
## `myCategory.fctr##:H.npnct28.log` 3.048611e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 2.874705e-06
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 2.816553e-06
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 2.789651e-06
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 2.666228e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 2.558353e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 2.501662e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 2.479709e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 2.479624e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.427709e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 2.384154e-06
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 2.352542e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 2.307417e-06
## `myCategory.fctr##:.clusterid.fctr6` 2.298298e-06
## `myCategory.fctrTStyle##` 2.284845e-06
## `myCategory.fctrScience#Health#` 2.267185e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 2.227300e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 2.137184e-06
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 2.072750e-06
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 1.981669e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.966548e-06
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 1.923278e-06
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 1.880414e-06
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 1.868903e-06
## `myCategory.fctr##:.clusterid.fctr15` 1.829058e-06
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 1.788485e-06
## `myCategory.fctrBusiness#Technology#` 1.684421e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 1.631228e-06
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.594630e-06
## `myCategory.fctr##:.clusterid.fctr16` 1.544245e-06
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 1.510938e-06
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 1.502867e-06
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 1.460159e-06
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 1.344872e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.284071e-06
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 1.230986e-06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.173238e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.169486e-06
## `myCategory.fctr##:.clusterid.fctr3` 1.099648e-06
## `myCategory.fctr##:.clusterid.fctr11` 1.066546e-06
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.030820e-06
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 1.014805e-06
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 1.011597e-06
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 9.605892e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 9.462511e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 9.142420e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 8.980943e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 8.429663e-07
## `myCategory.fctr##:PubDate.wkend` 8.100515e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 7.371228e-07
## `myCategory.fctrScience#Health#:PubDate.wkend` 7.067731e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 6.309615e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 6.009388e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 5.562643e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 4.536787e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.683459e-07
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 3.026130e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 2.250212e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 1.803294e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 1.350735e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.289087e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.259583e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 8.831372e-08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 8.370014e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 7.579186e-08
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 5.121507e-08
## `myCategory.fctr#Opinion#Room For Debate` 5.120522e-08
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 3.481721e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 3.401713e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 2.733009e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 2.454686e-08
## `myCategory.fctr##:.clusterid.fctr9` 2.376979e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 2.255188e-08
## `myCategory.fctrTStyle##:PubDate.wkend` 1.963644e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 1.950169e-08
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 1.443475e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 1.397598e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.371392e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.361231e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 1.332884e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 1.237475e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 1.210140e-08
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 1.050683e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 1.007279e-08
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 9.727372e-09
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 9.146561e-09
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 8.825715e-09
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 8.248431e-09
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 7.684549e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 6.719480e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 6.617133e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 6.569980e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 6.501329e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 6.496246e-09
## `myCategory.fctr#Multimedia#:PubDate.wkend` 6.054326e-09
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 4.515547e-09
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 3.679566e-09
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 3.115215e-09
## `myCategory.fctrTStyle##:H.npnct19.log` 2.423739e-09
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 2.060667e-09
## `myCategory.fctr#Multimedia#` 1.884324e-09
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 1.664927e-09
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 1.561113e-09
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 1.487501e-09
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 1.360716e-09
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 1.191237e-09
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 1.148850e-09
## `myCategory.fctrTravel#Travel#` 1.068490e-09
## `myCategory.fctrmyOther:WordCount.log.nonNA` 8.882188e-10
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 8.614427e-10
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 7.363973e-10
## `myCategory.fctrStyles##Fashion` 6.095459e-10
## `myCategory.fctrForeign#World#` 4.863020e-10
## myCategory.fctrmyOther 4.775128e-10
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 4.271925e-10
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 4.200071e-10
## `myCategory.fctr#U.S.#Education` 3.892163e-10
## `myCategory.fctrForeign#World#:H.npnct19.log` 3.078433e-10
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 2.706821e-10
## `myCategory.fctrmyOther:H.npnct19.log` 2.644820e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 1.818306e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 1.814126e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 1.813739e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 1.810528e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 1.803460e-10
## `myCategory.fctrBusiness#Crosswords/Games#` 1.800654e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 1.756708e-10
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 1.604906e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 1.520273e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 1.517043e-10
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 1.346338e-10
## `myCategory.fctr#Opinion#The Public Editor` 3.912648e-11
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 3.084007e-11
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 2.601071e-11
## `myCategory.fctrForeign#World#:PubDate.wkend` 6.774083e-14
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 4.770371e-14
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 4.121083e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 3.179932e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 3.147951e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 3.122164e-14
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 2.305464e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 1.404121e-14
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 9.838308e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 9.218443e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 9.013747e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 8.725064e-15
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 8.244360e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 7.798167e-15
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 6.286373e-15
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 6.182610e-15
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 5.359319e-15
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 3.815601e-15
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 3.478377e-15
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 1.839848e-15
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 1.831037e-15
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 8.519852e-16
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.598518e-16
## `myCategory.fctrmyOther:PubDate.wkend` 1.169430e-16
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 0.000000e+00
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` NA
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` NA
## importance
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` 8.806305e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 2.910547e+01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 3.516277e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 1.333186e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 1.146784e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 8.988177e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 1.532742e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 1.698803e+01
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 9.207735e+00
## `myCategory.fctrOpEd#Opinion#` 3.437797e-06
## WordCount.log.nonNA 3.566713e-06
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.587907e-06
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 1.903762e-06
## H.npnct19.log 1.593365e-06
## `myCategory.fctrMetro#N.Y. / Region#` 1.455912e-06
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 1.345482e-06
## `myCategory.fctr##:.clusterid.fctr2` 1.657311e-06
## `myCategory.fctrStyles#U.S.#` 1.912123e-06
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 1.649874e-06
## `myCategory.fctrBusiness#Business Day#Dealbook` 1.345989e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 6.781506e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 1.254966e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 5.367101e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 7.755804e-07
## `myCategory.fctr##:.clusterid.fctr10` 9.406888e-07
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 1.281813e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 8.409992e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 7.933944e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 9.040764e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 7.473132e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 8.341049e-07
## `myCategory.fctr##:.clusterid.fctr7` 7.218368e-07
## `myCategory.fctrCulture#Arts#` 1.125340e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 6.117557e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 6.285436e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 7.131851e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 5.327781e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 3.808715e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 8.419366e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 1.086536e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 6.573352e-07
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 6.758829e-07
## `myCategory.fctr##:.clusterid.fctr13` 1.544931e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 4.756228e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 5.705108e-07
## `myCategory.fctr##:.clusterid.fctr14` 5.392525e-07
## `myCategory.fctr##:.clusterid.fctr17` 3.707454e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 3.907341e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.402664e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 4.114890e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 7.135219e-07
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 6.445953e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 5.068323e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 5.094711e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 4.109930e-07
## `myCategory.fctr##:.clusterid.fctr8` 3.720519e-07
## `myCategory.fctrScience#Health#:H.npnct19.log` 4.530753e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 5.357326e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.278492e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 2.223980e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 3.456772e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 3.359718e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 4.013683e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.865807e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 7.474136e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.804215e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 5.936514e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 4.742299e-07
## `myCategory.fctrForeign#World#Asia Pacific` 2.628488e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 2.528193e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 3.846202e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 2.611478e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.299866e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 6.042813e-07
## `myCategory.fctr##:.clusterid.fctr4` 5.314840e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 3.064012e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 2.939185e-09
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 3.755332e-08
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 2.130207e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 2.174786e-07
## `myCategory.fctr##:.clusterid.fctr5` 5.103197e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 4.211730e-08
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 5.158852e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 5.153693e-07
## `myCategory.fctr##:.clusterid.fctr12` 3.307825e-07
## `myCategory.fctr##:H.npnct28.log` 1.435146e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 3.855351e-07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 2.121750e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 4.089487e-07
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 7.795444e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 3.109628e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 1.424081e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 7.527226e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 3.863492e-08
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.260798e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 2.618412e-07
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 5.485561e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 5.120551e-07
## `myCategory.fctr##:.clusterid.fctr6` 1.252376e-07
## `myCategory.fctrTStyle##` 1.000000e+02
## `myCategory.fctrScience#Health#` 5.642133e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 2.263094e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 3.797559e-07
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 3.778324e-08
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 2.556883e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.046234e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 1.532033e-07
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 1.195384e-07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 3.431991e-07
## `myCategory.fctr##:.clusterid.fctr15` 4.362696e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 4.828130e-07
## `myCategory.fctrBusiness#Technology#` 9.757127e-08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 2.061592e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.677761e-07
## `myCategory.fctr##:.clusterid.fctr16` 1.878795e-07
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 4.317077e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 3.163715e+01
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 1.094158e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 2.786863e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.981383e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 4.485101e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.242848e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.580509e-07
## `myCategory.fctr##:.clusterid.fctr3` 1.626328e-07
## `myCategory.fctr##:.clusterid.fctr11` 1.372074e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.810318e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 2.018071e-08
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 4.519356e-07
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 7.837659e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 3.327124e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 1.624747e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.524663e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 1.142168e-07
## `myCategory.fctr##:PubDate.wkend` 1.675502e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 7.020506e-08
## `myCategory.fctrScience#Health#:PubDate.wkend` 2.288969e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 2.195274e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 2.275094e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 1.254091e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 7.879616e-08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.283800e-07
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 4.385737e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 3.799885e-08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 9.561025e-08
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 5.774077e-08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.686291e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.218110e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 1.408366e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 1.838143e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 2.486961e-07
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 5.486278e+01
## `myCategory.fctr#Opinion#Room For Debate` 4.310726e+01
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 5.542535e-10
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 8.549049e-01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 1.151076e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 4.453252e+00
## `myCategory.fctr##:.clusterid.fctr9` 3.026444e-10
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 3.635097e+00
## `myCategory.fctrTStyle##:PubDate.wkend` 5.184820e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 1.061884e+01
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 2.755694e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 2.121290e-10
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.996518e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.848431e-10
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 2.176956e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 1.586154e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 2.216970e-10
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 3.873939e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 1.425362e-10
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 1.535767e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 1.304069e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.178777e-10
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 1.660105e-11
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 9.035012e-08
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 7.274620e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 7.140214e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 6.958738e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 6.851945e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 7.044608e-10
## `myCategory.fctr#Multimedia#:PubDate.wkend` 6.468341e-11
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 5.752816e-11
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 3.672539e-09
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 1.945557e+00
## `myCategory.fctrTStyle##:H.npnct19.log` 8.675356e+00
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 9.823238e-10
## `myCategory.fctr#Multimedia#` 2.932612e-11
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 4.103606e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 2.317092e-11
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 1.714009e-11
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 3.004919e-01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 1.730219e-11
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 1.947941e-11
## `myCategory.fctrTravel#Travel#` 4.157460e-10
## `myCategory.fctrmyOther:WordCount.log.nonNA` 1.008287e+01
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 6.786115e+00
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.186972e-10
## `myCategory.fctrStyles##Fashion` 1.108835e+01
## `myCategory.fctrForeign#World#` 1.279560e-11
## myCategory.fctrmyOther 1.039854e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 4.454489e-12
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 2.107963e+01
## `myCategory.fctr#U.S.#Education` 4.295877e-10
## `myCategory.fctrForeign#World#:H.npnct19.log` 1.180297e-12
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 2.403129e-12
## `myCategory.fctrmyOther:H.npnct19.log` 1.018993e+01
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 4.367209e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 4.343860e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 4.341100e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 4.337978e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 4.335122e-10
## `myCategory.fctrBusiness#Crosswords/Games#` 4.333645e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 4.112374e-09
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 1.191574e-12
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 4.144475e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 4.136567e-09
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 8.764559e-10
## `myCategory.fctr#Opinion#The Public Editor` 4.170625e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 4.161807e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 4.154666e-09
## `myCategory.fctrForeign#World#:PubDate.wkend` 1.558360e-11
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 1.699658e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 9.264147e-13
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.099928e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.581134e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 6.925538e-01
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 7.881611e-11
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 2.129981e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.772638e+01
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 7.792280e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 8.541457e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 7.144203e-10
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 2.625920e-12
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 8.854467e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 6.444580e-10
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 1.306532e-12
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 6.489696e-11
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000000e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 1.643184e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 3.993233e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.609469e+01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 4.281107e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.437178e+01
## `myCategory.fctrmyOther:PubDate.wkend` 1.478378e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 1.308512e+00
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` 6.418581e-11
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 4.852603e-13
## Final.glm.importance
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.wkend` 8.806305e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 2.910547e+01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log.nonNA` 3.516277e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 1.333186e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 1.146784e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 8.988177e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 1.532742e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 1.698803e+01
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 9.207735e+00
## `myCategory.fctrOpEd#Opinion#` 3.437797e-06
## WordCount.log.nonNA 3.566713e-06
## `myCategory.fctrOpEd#Opinion#:WordCount.log.nonNA` 2.587907e-06
## `myCategory.fctrCulture#Arts#:PubDate.wkend` 1.903762e-06
## H.npnct19.log 1.593365e-06
## `myCategory.fctrMetro#N.Y. / Region#` 1.455912e-06
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log.nonNA` 1.345482e-06
## `myCategory.fctr##:.clusterid.fctr2` 1.657311e-06
## `myCategory.fctrStyles#U.S.#` 1.912123e-06
## `myCategory.fctrStyles#U.S.#:WordCount.log.nonNA` 1.649874e-06
## `myCategory.fctrBusiness#Business Day#Dealbook` 1.345989e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 6.781506e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log.nonNA` 1.254966e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 5.367101e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 7.755804e-07
## `myCategory.fctr##:.clusterid.fctr10` 9.406888e-07
## `myCategory.fctrCulture#Arts#:WordCount.log.nonNA` 1.281813e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 8.409992e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 7.933944e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 9.040764e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 7.473132e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.wkend` 8.341049e-07
## `myCategory.fctr##:.clusterid.fctr7` 7.218368e-07
## `myCategory.fctrCulture#Arts#` 1.125340e-06
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 6.117557e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 6.285436e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 7.131851e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 5.327781e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 3.808715e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 8.419366e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 1.086536e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 6.573352e-07
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 6.758829e-07
## `myCategory.fctr##:.clusterid.fctr13` 1.544931e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 4.756228e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 5.705108e-07
## `myCategory.fctr##:.clusterid.fctr14` 5.392525e-07
## `myCategory.fctr##:.clusterid.fctr17` 3.707454e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 3.907341e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.402664e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 4.114890e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 7.135219e-07
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log.nonNA` 6.445953e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 5.068323e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 5.094711e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 4.109930e-07
## `myCategory.fctr##:.clusterid.fctr8` 3.720519e-07
## `myCategory.fctrScience#Health#:H.npnct19.log` 4.530753e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 5.357326e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.278492e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` 2.223980e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 3.456772e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 3.359718e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 4.013683e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.865807e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 7.474136e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.804215e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 5.936514e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 4.742299e-07
## `myCategory.fctrForeign#World#Asia Pacific` 2.628488e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 2.528193e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 3.846202e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 2.611478e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.299866e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 6.042813e-07
## `myCategory.fctr##:.clusterid.fctr4` 5.314840e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 3.064012e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 2.939185e-09
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 3.755332e-08
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 2.130207e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 2.174786e-07
## `myCategory.fctr##:.clusterid.fctr5` 5.103197e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.wkend` 4.211730e-08
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 5.158852e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 5.153693e-07
## `myCategory.fctr##:.clusterid.fctr12` 3.307825e-07
## `myCategory.fctr##:H.npnct28.log` 1.435146e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 3.855351e-07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log.nonNA` 2.121750e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 4.089487e-07
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 7.795444e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 3.109628e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.wkend` 1.424081e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 7.527226e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 3.863492e-08
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.260798e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 2.618412e-07
## `myCategory.fctrScience#Health#:WordCount.log.nonNA` 5.485561e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 5.120551e-07
## `myCategory.fctr##:.clusterid.fctr6` 1.252376e-07
## `myCategory.fctrTStyle##` 1.000000e+02
## `myCategory.fctrScience#Health#` 5.642133e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 2.263094e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 3.797559e-07
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 3.778324e-08
## `myCategory.fctrBusiness#Technology#:PubDate.wkend` 2.556883e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.046234e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 1.532033e-07
## `myCategory.fctrBusiness#Technology#:WordCount.log.nonNA` 1.195384e-07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.wkend` 3.431991e-07
## `myCategory.fctr##:.clusterid.fctr15` 4.362696e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 4.828130e-07
## `myCategory.fctrBusiness#Technology#` 9.757127e-08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 2.061592e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.677761e-07
## `myCategory.fctr##:.clusterid.fctr16` 1.878795e-07
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 4.317077e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 3.163715e+01
## `myCategory.fctr#Multimedia#:WordCount.log.nonNA` 1.094158e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 2.786863e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.981383e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 4.485101e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.242848e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.580509e-07
## `myCategory.fctr##:.clusterid.fctr3` 1.626328e-07
## `myCategory.fctr##:.clusterid.fctr11` 1.372074e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.810318e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 2.018071e-08
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 4.519356e-07
## `myCategory.fctrTStyle##:WordCount.log.nonNA` 7.837659e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 3.327124e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 1.624747e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.524663e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 1.142168e-07
## `myCategory.fctr##:PubDate.wkend` 1.675502e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 7.020506e-08
## `myCategory.fctrScience#Health#:PubDate.wkend` 2.288969e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 2.195274e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 2.275094e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 1.254091e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 7.879616e-08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.283800e-07
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 4.385737e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 3.799885e-08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 9.561025e-08
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 5.774077e-08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.686291e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.218110e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 1.408366e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 1.838143e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 2.486961e-07
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log.nonNA` 5.486278e+01
## `myCategory.fctr#Opinion#Room For Debate` 4.310726e+01
## `myCategory.fctrTravel#Travel#:WordCount.log.nonNA` 5.542535e-10
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 8.549049e-01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 1.151076e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 4.453252e+00
## `myCategory.fctr##:.clusterid.fctr9` 3.026444e-10
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 3.635097e+00
## `myCategory.fctrTStyle##:PubDate.wkend` 5.184820e+01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 1.061884e+01
## `myCategory.fctrStyles#U.S.#:PubDate.wkend` 2.755694e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 2.121290e-10
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.996518e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.848431e-10
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 2.176956e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 1.586154e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 2.216970e-10
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.wkend` 3.873939e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 1.425362e-10
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 1.535767e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` 1.304069e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.178777e-10
## `myCategory.fctrTravel#Travel#:PubDate.wkend` 1.660105e-11
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 9.035012e-08
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 7.274620e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 7.140214e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 6.958738e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 6.851945e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 7.044608e-10
## `myCategory.fctr#Multimedia#:PubDate.wkend` 6.468341e-11
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct28.log` 5.752816e-11
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 3.672539e-09
## `myCategory.fctr#Opinion#Room For Debate:PubDate.wkend` 1.945557e+00
## `myCategory.fctrTStyle##:H.npnct19.log` 8.675356e+00
## `myCategory.fctr#U.S.#Education:WordCount.log.nonNA` 9.823238e-10
## `myCategory.fctr#Multimedia#` 2.932612e-11
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 4.103606e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 2.317092e-11
## `myCategory.fctrForeign#World#:WordCount.log.nonNA` 1.714009e-11
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 3.004919e-01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 1.730219e-11
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 1.947941e-11
## `myCategory.fctrTravel#Travel#` 4.157460e-10
## `myCategory.fctrmyOther:WordCount.log.nonNA` 1.008287e+01
## `myCategory.fctrStyles##Fashion:WordCount.log.nonNA` 6.786115e+00
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.186972e-10
## `myCategory.fctrStyles##Fashion` 1.108835e+01
## `myCategory.fctrForeign#World#` 1.279560e-11
## myCategory.fctrmyOther 1.039854e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 4.454489e-12
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 2.107963e+01
## `myCategory.fctr#U.S.#Education` 4.295877e-10
## `myCategory.fctrForeign#World#:H.npnct19.log` 1.180297e-12
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 2.403129e-12
## `myCategory.fctrmyOther:H.npnct19.log` 1.018993e+01
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 4.367209e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 4.343860e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 4.341100e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 4.337978e-10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 4.335122e-10
## `myCategory.fctrBusiness#Crosswords/Games#` 4.333645e-10
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 4.112374e-09
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 1.191574e-12
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 4.144475e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 4.136567e-09
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log.nonNA` 8.764559e-10
## `myCategory.fctr#Opinion#The Public Editor` 4.170625e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 4.161807e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 4.154666e-09
## `myCategory.fctrForeign#World#:PubDate.wkend` 1.558360e-11
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 1.699658e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 9.264147e-13
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.099928e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.581134e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 6.925538e-01
## `myCategory.fctr#U.S.#Education:H.npnct28.log` 7.881611e-11
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 2.129981e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.772638e+01
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 7.792280e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 8.541457e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 7.144203e-10
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 2.625920e-12
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 8.854467e-10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 6.444580e-10
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 1.306532e-12
## `myCategory.fctr#U.S.#Education:PubDate.wkend` 6.489696e-11
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000000e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 1.643184e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 3.993233e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.609469e+01
## `myCategory.fctrStyles##Fashion:PubDate.wkend` 4.281107e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.437178e+01
## `myCategory.fctrmyOther:PubDate.wkend` 1.478378e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 1.308512e+00
## `myCategory.fctr#Opinion#The Public Editor:PubDate.wkend` 6.418581e-11
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 4.852603e-13print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 1355.019 1364.552 9.533
## 17 display.session.info 10 0 1364.553 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 398.122 1066.479 668.357
## 14 fit.data.training 8 0 1097.571 1342.225 244.654
## 10 fit.models 7 0 235.830 398.122 162.292
## 6 extract.features 3 0 46.478 195.058 148.580
## 8 select.features 5 0 210.115 234.862 24.747
## 12 fit.models 7 2 1066.479 1090.815 24.337
## 2 inspect.data 2 0 9.956 31.672 21.716
## 7 cluster.data 4 0 195.058 210.115 15.057
## 15 fit.data.training 8 1 1342.225 1355.019 12.794
## 16 predict.data.new 9 0 1355.019 1364.552 9.533
## 4 manage.missing.data 2 2 37.920 46.451 8.531
## 13 fit.models 7 3 1090.816 1097.571 6.755
## 3 cleanse.data 2 1 31.672 37.919 6.248
## 1 import.data 1 0 8.938 9.955 1.017
## 9 partition.data.training 6 0 234.863 235.829 0.966
## 5 encode.data 2 3 46.451 46.478 0.027
## duration
## 11 668.357
## 14 244.654
## 10 162.292
## 6 148.580
## 8 24.747
## 12 24.336
## 2 21.716
## 7 15.057
## 15 12.794
## 16 9.533
## 4 8.531
## 13 6.755
## 3 6.247
## 1 1.017
## 9 0.966
## 5 0.027
## [1] "Total Elapsed Time: 1,364.552 secs"
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_rpart 4 0 591.029 1066.471 475.442
## 2 fit.models_1_glm 2 0 406.602 526.510 119.908
## 3 fit.models_1_bayesglm 3 0 526.510 591.028 64.518
## 1 fit.models_1_bgn 1 0 406.586 406.602 0.016
## duration
## 4 475.442
## 2 119.908
## 3 64.518
## 1 0.016
## [1] "Total Elapsed Time: 1,066.471 secs"
## R version 3.2.0 (2015-04-16)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] gdata_2.16.1 arm_1.8-5 lme4_1.1-7
## [4] Matrix_1.2-1 MASS_7.3-40 rpart.plot_1.5.2
## [7] rpart_4.1-9 ROCR_1.0-7 gplots_2.17.0
## [10] caTools_1.17.1 dynamicTreeCut_1.62 proxy_0.4-14
## [13] tm_0.6-1 NLP_0.1-7 stringr_1.0.0
## [16] mice_2.22 Rcpp_0.11.6 plyr_1.8.2
## [19] zoo_1.7-12 sqldf_0.4-10 RSQLite_1.0.0
## [22] DBI_0.3.1 gsubfn_0.6-6 proto_0.3-10
## [25] reshape2_1.4.1 doMC_1.3.3 iterators_1.0.7
## [28] foreach_1.4.2 doBy_4.5-13 survival_2.38-1
## [31] caret_6.0-47 ggplot2_1.0.1 lattice_0.20-31
##
## loaded via a namespace (and not attached):
## [1] class_7.3-12 gtools_3.5.0 digest_0.6.8
## [4] slam_0.1-32 BradleyTerry2_1.0-6 chron_2.3-45
## [7] coda_0.17-1 evaluate_0.7 e1071_1.6-4
## [10] minqa_1.2.4 SparseM_1.6 car_2.0-25
## [13] nloptr_1.0.4 rmarkdown_0.6.1 labeling_0.3
## [16] splines_3.2.0 munsell_0.4.2 compiler_3.2.0
## [19] mgcv_1.8-6 htmltools_0.2.6 nnet_7.3-9
## [22] codetools_0.2-11 randomForest_4.6-10 brglm_0.5-9
## [25] bitops_1.0-6 nlme_3.1-120 gtable_0.1.2
## [28] magrittr_1.5 formatR_1.2 scales_0.2.4
## [31] KernSmooth_2.23-14 stringi_0.4-1 RColorBrewer_1.1-2
## [34] tools_3.2.0 abind_1.4-3 pbkrtest_0.4-2
## [37] yaml_2.1.13 colorspace_1.2-6 knitr_1.10.5
## [40] quantreg_5.11